Optimization

Open Access Article

1 - Speed Estimation and Sensorless Torque Optimization of Single Phase Induction Motor
S. Vaez-Zadeh - Personal page A. Payman

20.1001.1.16823745.1382.1.2.2.5

Recently, performance improvement and speed control of Single-Phase Induction Motors (SPIMs) have been paid attention. These aims is required the machine speed. In this paper, a method is proposed to estimate the SPIMs speed, and then, its application in torque optimiza More

Recently, performance improvement and speed control of Single-Phase Induction Motors (SPIMs) have been paid attention. These aims is required the machine speed. In this paper, a method is proposed to estimate the SPIMs speed, and then, its application in torque optimization of the machine is investigated. For this purpose, the motor speed is obtained in terms of the motor parameters and stator flux linkage components by use of the SPIMs equations in stationary reference frame. By obtaining the flux linkage from motor windings voltages and currents, the motor speed is estimated desirably. Then the estimated speed is used to increase the average torque, to decrease the pulsation torque and to optimize the motor torque. After that, the simulation results in condition of using the real speed is compared with the estimated speed one. The low simulation error proves the validity of the proposed method Manuscript profile

Open Access Article

2 - Multi-Objective Particle Swarm Classifier
Seyed-Hamid Zahiri

20.1001.1.16823745.1385.4.8.6.2

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The propos More

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not have any over-fitting and over-learning problems. Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem. The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices. Manuscript profile

Open Access Article

3 - Investigation and Evaluation of Adaptive Nulling Methods of Array Antennas Using Genetic Algorithm
S. Jam M. Delroshan

20.1001.1.16823745.1387.6.13.5.5

This paper describes an approach to adaptive nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase-shifters to minimize the total output power of the array. Also, some other criterions such as Mean Square E More

This paper describes an approach to adaptive nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase-shifters to minimize the total output power of the array. Also, some other criterions such as Mean Square Error and Signal to Interference plus Noise Ratio are used and compared with each other. Using the least significant bits results in small perturbation in the main beam of the radiation pattern and puts the nulls in the direction of the interferences. Double search and weighted mutation are used to reduce the complexity of the algorithm. Also, the performance of genetic algorithm is compared with MPDR which is an optimum technique for beamforming. Finally, it is shown that the genetic algorithm performs superior to MPDR. Manuscript profile

Open Access Article

4 - A Two-Stage Method for Classifiers Combination
S. H. Nabavi Karizi E. Kabir

20.1001.1.16823745.1387.6.13.7.7

Ensemble learning is an effective machine learning method that improves the classification performance. In this method, the outputs of multiple classifiers are combined so that the better results can be attained. As different classifiers may offer complementary informat More

Ensemble learning is an effective machine learning method that improves the classification performance. In this method, the outputs of multiple classifiers are combined so that the better results can be attained. As different classifiers may offer complementary information about the classification, combining classifiers, in an efficient way, can achieve better results than any single classifier. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. In this paper, we propose a two-stage method for classifiers combination. In the first stage, by mixture of experts strategy we produce different classifiers and in the second stage by using particle swarm optimization (PSO), we find the optimal weights for linear combination of them. Experimental results on different data sets show that proposed method outperforms the independent training and mixture of experts methods. Manuscript profile

Open Access Article

5 - Application of PSO Algorithm in Economic and Emission Dispatch with Non-Smooth Cost Functions by Considering Transmission Losses and System Constraints
R. Hooshmand M. Parastegari

20.1001.1.16823745.1387.6.15.1.5

Precise and practical based economic dispatch is one of the most important problems in power systems. Thus, this paper proposes usage of particle swarm optimization (PSO) algorithm for solving economic dispatch problem. In this study real constraints of economic dispatc More

Precise and practical based economic dispatch is one of the most important problems in power systems. Thus, this paper proposes usage of particle swarm optimization (PSO) algorithm for solving economic dispatch problem. In this study real constraints of economic dispatch problem are considered. For this purpose, it has been considered that the fuel cost function is a non-smooth one. On the other hand, reduction of the pollutants that is emitted from fossil fuel power plants is one of the goals of the optimization problem, so that we fulfill economic and emission dispatch at the same time for solving practical and optimum economic dispatch problem with consideration of many constraints in the operating point and transmission losses, these constraints are included in the proposed method. Finally, simulation results of the proposed method for economic dispatch are compared with those of the other methods such as tabu search, genetic algorithm, and artificial neural network. The results clearly show that the proposed method gives global optimum and fast solution compared to the other methods. Manuscript profile

Open Access Article

6 - Distributed Generation Sources Placement in Electric Power Distribution Networks under Uncertainty
H. Falaghi M. Parsa-Moghaddam

20.1001.1.16823745.1388.7.17.1.6

This paper presents a new multiobjective model for optimal placement of distributed generation sources in electric distribution network under load and market price uncertainties that finds out the non-dominated multiobjective solutions corresponding to the simultaneous More

This paper presents a new multiobjective model for optimal placement of distributed generation sources in electric distribution network under load and market price uncertainties that finds out the non-dominated multiobjective solutions corresponding to the simultaneous minimization of economic cost, technical risks, and economical risk due to uncertainties. Fuzzy sets theory is used to model the uncertainties. The proposed model is solved using a specialized genetic algorithm as the optimization tool. The performance of the proposed approach is assessed and appreciated by case study on a typical distribution network. Manuscript profile

Open Access Article

7 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
N. Ghanbari S. M. Razavi S. H. Nabavi Karizi

20.1001.1.16823745.1390.9.1.4.1

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, us More

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, using binary gravitational search algorithm. Implementation results show that the use of intelligent methods is well able to choose the most effective features for this recognition system. The results of the proposed method in comparison with other similar methods based on genetic algorithm and binary particle method of optimizing indicates the effective performance of the proposed method. Manuscript profile

Open Access Article

8 - Economical Optimization of Capacity and Operational Strategy for Combined Heat and Power Systems
M. Hajinazari

20.1001.1.16823745.1390.9.4.5.8

An optimization method has been developed to determine the optimal capacities for the CHP and boiler such that thermal and electrical energy demands can be satisfied with high cost efficiency. The proposed method offers an operational strategy in order to determine the More

An optimization method has been developed to determine the optimal capacities for the CHP and boiler such that thermal and electrical energy demands can be satisfied with high cost efficiency. The proposed method offers an operational strategy in order to determine the optimum value for boiler and CHP capacities which maximize an objective function based on the net present value (NPV). The reduction in operational strategy expenses arising from the monetary cost of the credit attainable by air pollution reduction is also taken into account in evaluation of the objective function. The optimal value for boiler and CHP capacities and the resulting projection for the optimal value of the objective function are derived using a hybrid optimization method involving the particle swarm optimization (PSO) and the linear programming algorithms. The viability of the proposed method is demonstrated by analyzing the decision to construct a CHP system for a typical hospital. Manuscript profile

Open Access Article

9 - Risk-based Static and Dynamics Security Assessment and Its Enhancement with Particle Swarm Optimization Generation Realloca
M. Saeedi H. Seifi

20.1001.1.16823745.1391.10.1.3.7

Security assessment is traditionally checked using a deterministic criterion. Based on that, the system may be considered as secured or unsecured. If an unsecured condition is detected, preventive actions are foreseen to make it secure. Recently, risk based security as More

Security assessment is traditionally checked using a deterministic criterion. Based on that, the system may be considered as secured or unsecured. If an unsecured condition is detected, preventive actions are foreseen to make it secure. Recently, risk based security assessment is used in power systems. In this paper, risk-based static and dynamic security assessment is proposed and a new transient stability index is defined. In this paper, the risk index is used as an objective function in the generation reallocation algorithm. In this algorithm, the security is maintained using the generation reallocation. The algorithm is tested on IEEE 24-bus test system and its capabilities are assessed in comparison with a traditional OPF, in which the security is maintained based on a deterministic criterion. Particle Swarm Optimization (PSO) algorithm is used as the optimization tool. Manuscript profile

Open Access Article

10 - Designing Optimal Fuzzy Classifier Using Particle Swarm Optimization
Seyed-Hamid Zahiri

20.1001.1.16823745.1391.10.4.5.5

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide More

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide range high dimensional search space, which makes heuristic methods some suitable candidates to solve this problem (designing optimal fuzzy parameters). In this paper, a method is described for this purpose. In presented technique, all fuzzy parameters of a fuzzy classifier, are interpreted in structure of particles and PSO algorithm is employed to find the optimal one. Extensive experimental results on well-known benchmarks and practical pattern recognition problem (automatic target recognition) demonstrate the effectiveness of the proposed method. Manuscript profile

Open Access Article

11 - Design Improvement of Synchronous Reluctance Motor Geometry, Using Neural-Network, Genetic Algorithm and Finite Element Method
M. Haghparast S. Taghipour Boroujeni A. Kargar

20.1001.1.16823745.1392.11.1.4.5

appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In More

appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In this paper an intelligent- method have been used to optimizing the design of SynRel motors based on magnetic saliency ratio. To achieve this aim, all of the motor parameters including stator geometry, axial length of machine, winding type, and number of flux barriers in rotor are assumed constant and just position of the rotor flux barriers are optimized. These positions have been defined by six parameters. Changing these parameters, the magnetic saliency of machine is calculated by finite element analysis (FEA). Using these values to train a neural network (NN), a modeling function is obtained for magnetic saliency of SynRel machine. Considering this NN as the target function in genetic algorithm (GA), the parameters of SynRel machine have been optimized and the best rotor structure with highest magnetic saliency has been obtained. Finally the abilities of NN in correct estimation of magnetic saliency and motor synchronization were approved by FEA and dynamic simulation. Manuscript profile

Open Access Article

12 - Unsupervised Image Clustering Using Central Force Optimization Algorithm Unsupervised Image Clustering Using Central Force Optimization Algorithm
M. H. Mozafari Maref Seyed-Hamid Zahiri

20.1001.1.16823745.1392.11.2.6.9

Central Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In More

Central Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In the presented method, each probe includes the information of center of the clusters, and fitness function contains both inter-distance and intra-distance of the samples. Extensive experimental results show that the proposed CFO-clustering outperforms other similar clustering algorithms which were designed based on the evolutionary techniques. Manuscript profile

Open Access Article

13 - Introducing a New Version of Binary Ant Colony Algorithm to Solve the Problem of Feature Selection
S. Kashef H. Nezamabadi-pour

20.1001.1.16823745.1393.12.4.5.9

The use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of More

The use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of binary ant colony optimization, which attempts to solve the problems of ACO and BACO algorithms by combination of these two. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. Manuscript profile

Open Access Article

14 - Optimization of the Dynamic Response and the Input Current THD for PFC Rectifier Based on Boost Converter Using SPEA and NSGA-II Algorithms
H. Abolhasani S. M. R. Rafiei

20.1001.1.16823745.1394.13.1.5.0

In single-stage single-phase power factor correction converters, the time of dynamic response and input current THD are in conflict with each other. The main purpose of this paper is to improve the dynamic response of the converter along with reducing its input current More

In single-stage single-phase power factor correction converters, the time of dynamic response and input current THD are in conflict with each other. The main purpose of this paper is to improve the dynamic response of the converter along with reducing its input current THD. To achieve these goals, two multi-objective optimization methods based on evolutionary algorithms including; SPEA and NSGA-II were used to design a PI compensator coefficients in the indirect current control technique for PFC rectifier. The integral and fractional-order PI compensators were designed, respectively. The obtained results showed the superiority of the fractional-order PI compensator. To investigate the optimization problem, the dynamic response to changes in load and reference voltage was considered. The comparison between the optimization algorithms showed that each algorithm may have better performance than the other one according to the used objective functions. It means that neither had an absolute superiority. Manuscript profile

Open Access Article

15 - Wind Farm Layout Optimization with Emphasis on the Wake Effect
A. Farajipoor F. Faghihi R. Sharifi

20.1001.1.16823745.1394.13.1.9.4

Construction of wind farms rise for wind energy capture as a renewable energy around the world. The purpose of wind farm layout optimization, absorb maximum energy from wind farms. In this paper, a new hybrid algorithm is presented to maximize the expected energy output More

Construction of wind farms rise for wind energy capture as a renewable energy around the world. The purpose of wind farm layout optimization, absorb maximum energy from wind farms. In this paper, a new hybrid algorithm is presented to maximize the expected energy output. Considerations of algorithm wake loss, which is based on wind turbine location and wind direction. The proposed model is illustrated with a scenario of the wind speed and its direction distribution of windy sites and is compared with ant colony algorithm and evolutionary strategy algorithm in six steps layout. The results show that the combination of ant colony algorithm and genetic algorithm performs better than existing strategies based on maximum values of the expected energy output and wake loss. Manuscript profile

Open Access Article

16 - Multi Objective Network Reconfiguration for Distribution System with Micro-Grids Power Exchange using Max-Min Fuzzy Method and Particle Swarm Optimization Algorithm
A. Fattahi Meyabadi H. Sohrabiani

20.1001.1.16823745.1394.13.3.4.3

A group of small generators and energy storages in the low or medium voltage distribution systems beside of consumers emerge to a new power system called micro grid. Micro grids are designed to have secure and economic operation isolated and connected to the network and More

A group of small generators and energy storages in the low or medium voltage distribution systems beside of consumers emerge to a new power system called micro grid. Micro grids are designed to have secure and economic operation isolated and connected to the network and exchange electrical energy with distribution system. Hence, they may impact on planning and scheduling of distribution systems. In this case, network reconfiguration is a considerable issue after presenting of micro grids to the system. In the previous studies regarding to this issue, micro grid is considered as a distributed generation which should only produce electricity to the network. In this paper, micro grid is modeled as a power exchanger in the distribution network to study the effect of it on the network reconfiguration. For this purpose, reconfiguration is formulated as a multi objective optimization problem using max-min fuzzy method. In this problem, power loss reduction and load balancing among feeders are two independent objectives and voltage profile, lines congestion, radial network structure and load flow are equality and inequality constraints. Particle swarm algorithm is applied to solve the optimization problem and the reconfiguration over two 33 and 70 buses IEEE test network is shown. Results demonstrate that replacing traditional distribution systems by modern active networks and exchanging power with micro grids can lead to increase the reliability of system and more economic operation. Manuscript profile

Open Access Article

17 - Analytical Stator Design for Reducing the Cogging Torque in Surface-Mounted PM Motors
M. R. Alizadeh Pahlavani V. Zamani Faradonbe

20.1001.1.16823745.1395.14.2.5.9

We present an analytical method for the calculation of cogging torque in surface permanent-magnet (PM) motors. The cogging torque is calculated by integrating the Maxwell stress tensor inside the air gap. The stator design techniques are applied to reduce the cogging to More

We present an analytical method for the calculation of cogging torque in surface permanent-magnet (PM) motors. The cogging torque is calculated by integrating the Maxwell stress tensor inside the air gap. The stator design techniques are applied to reduce the cogging torque in SPM motors. The used techniques are stator dummy slots, teeth pairing and stator slot skewing. The direct search method is used to find the optimum geometry in the mentioned methods. Finally, the validity of the proposed model and the obtained results are verified with Finite Element Analysis. Manuscript profile

Open Access Article

18 - Multi-Objective Optimization of Surface-Mounted PM Machines Using an Analytical Model for the Pole-Shifting Method
V. Zamani Faradonbe S. Taghipour Boroujeni

20.1001.1.16823745.1395.14.2.6.0

In the presented work an analytical model is developed for the pole-shifting method in the surface-mounted PM machine at no-load condition. The machine cogging torque and the harmonic spectrum of the air gap flux density are most no-load indexes of the machine performan More

In the presented work an analytical model is developed for the pole-shifting method in the surface-mounted PM machine at no-load condition. The machine cogging torque and the harmonic spectrum of the air gap flux density are most no-load indexes of the machine performance. It is shown that, although, the pole-shifting reduces the machine cogging torque; it destroyed the half-odd symmetry in the PMs and produces even harmonics in the air gap flux density. The even harmonics of the air gap flux density, results in undesired torque pulsations. Using the developed analytical model and the direct search method a multi-objective optimization is carried out for the machine cogging torque and the total harmonic distortion of the air gap flux density. Since, the considered variables are not in a same unite; a normalized technique is applied. Finally, the developed model and the obtained results are verified by finite element analysis. Manuscript profile

Open Access Article

19 - Quantum-Logic Synthesis Using Improved Block-Based Approach
K. Marjoei M. Houshmand M. Saheb Zamani M. Sedighi

20.1001.1.16823745.1395.14.3.14.0

Quantum-logic synthesis refers to generating a quantum circuit for a given arbitrary quantum gate according to a specific universal gate library implementable in quantum technologies. Previously, an approach called block-based quantum decomposition (BQD) has been propos More

Quantum-logic synthesis refers to generating a quantum circuit for a given arbitrary quantum gate according to a specific universal gate library implementable in quantum technologies. Previously, an approach called block-based quantum decomposition (BQD) has been proposed to synthesize quantum circuits by using a combination of two well-known quantum circuit synthesis methods, namely, quantum Shannon decomposition (QSD) and cosine-sine decomposition (CSD). In this paper, an improved block-based quantum decomposition (IBQD) is proposed. IBQD is a parametric approach and explores a larger space than CSD, QSD, and BQD to obtain best results for various synthesis cost metrics. IBQD cost functions for synthesis are calculated in terms of different synthesis cost metrics with respect to the parameters of the proposed approach. Furthermore, in order to find optimum results according to these functions, IBQD synthesis approach is defined as a constrained-optimization model. The results show that IBQD can lead to the minimum total gate cost among all the proposed approaches for the specific case of 4-qubit quantum circuit synthesis. Moreover, for the first time, the depth costs of the CSD, QSD, BQD, and IBQD synthesis approaches are evaluated and it is shown that IBQD makes a trade-off between the total gates and depth costs for the synthesized quantum circuits. Manuscript profile

Open Access Article

20 - Design, Optimization, and Finite Element Analysis of a Disk-Type Permanent Magnet Synchronous Motor
S. A. Seyedi Seadati A. Halvaei Niasar

This paper proposes to design, optimization and finite element simulation of an axial-flux, super-high speed, permanent magnet motor. The target motor with 0.5 hp rated power at speed of 60,000 rpm is used in a special industrial application. Based on nominal specificat More

This paper proposes to design, optimization and finite element simulation of an axial-flux, super-high speed, permanent magnet motor. The target motor with 0.5 hp rated power at speed of 60,000 rpm is used in a special industrial application. Based on nominal specifications of the motor and using analytical relations of motor design, the design calculations, sizing and motor dimensions are investigated. Due to special application of the target motor that needs to the demanded torque with minimum current and copper losses, the dimensions and design specifications of motor is optimized via genetic algorithm based on a torque per ampere cost function. Optimization algorithm determines the optimum value of airgap, permanent magnet flux density, current density and turns number of stator windings. To demonstrate of analytical design and optimization results, using 3-D model of motor in Maxwell software, finite element analysis are carried out in Magneto-static and Transient modes. The FEM simulation results confirm the analytical design results. Moreover, they show the significant reduction in RMS current and copper loss at rated torque. There is a good agreement between the values of torque, motor efficiency, and flux density resulted from both methods. Manuscript profile

Open Access Article

21 - Optimizing Quantum Circuits by One-Way Quantum Computation Model Based on Pattern Geometries
M. Eslamy M. Saheb Zamani M. Sedighi M. Houshmand

20.1001.1.16823745.1395.14.4.10.8

A fundamentally quantum model of computation based on quantum entanglement and quantum measurement is called one-way quantum computation model (1WQC). Computations are shown by measurement patterns (or simply patterns) in this model where an initial highly entangled sta More

A fundamentally quantum model of computation based on quantum entanglement and quantum measurement is called one-way quantum computation model (1WQC). Computations are shown by measurement patterns (or simply patterns) in this model where an initial highly entangled state called a graph state is used to perform universal quantum computations. This graph together with the set of its input and output qubits is called the geometry of the pattern. Moreover, some optimization techniques have been introduced to simplify patterns. Previously, the 1WQC model has been applied to optimize quantum circuits. An approach for parallelizing quantum circuits has been proposed which takes a quantum circuit and then produces the corresponding pattern after performing the proposed optimization techniques for this model. Then it translates the optimized 1WQC patterns back to quantum circuits to parallelize the initial quantum circuit by using a set of rewriting rules. To improve previous works, in this paper, a new automatic approach is proposed to optimize patterns based on their geometries instead of using rewriting rules by applying optimization techniques simultaneously. Moreover, the optimized pattern is translated back to a quantum circuit and then this circuit is simplified by decreasing the number of auxiliary qubits. Results show that the quantum circuit cost metrics of the proposed approach is improved as compared to the previous ones. Manuscript profile

Open Access Article

22 - Classification and Phishing Websites Detection by Fuzzy Rules and Modified Inclined Planes Optimization
M. Abdolrazzagh-Nezhad

20.1001.1.16823745.1395.14.4.12.0

One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibili More

One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibility to effective attribute selection in the procedure of phishing websites detection, non- dynamic behavior of classification algorithm on target websites and also no attention to reduce the amount of computation for the large number of websites are the main gaps of these methods. To achieve the above-mentioned objectives, a new dynamic mechanism is planned to flexible attribute reduction based on designing threshold change of assessment in this paper. Then inclined planes optimization algorithm is memorized based soft reducing the effect of the embedded memory though high iterations and 12 fuzzy rules are defined in a fuzzy inference system for intelligent dynamiting the algorithm. The experimental results of the proposed intelligent algorithm and the comparison the algorithms with the best available algorithms; demonstrate the ability of the modified inclined planes optimization algorithm to detect phishing websites and satisfy the above mentioned objectives. Manuscript profile

Open Access Article

23 - A New Variance-Based Method for Solving Stochastic Graph Optimization Problem Using Learning Automata
M. R. Mollakhalili Meybodi M. R. Meybodi

20.1001.1.16823745.1396.15.1.9.8

In this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better a More

In this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better adaptation to changes of environment. As a result, the proposed method can produce better response to learning Automata actions. The proposed method, by entering a noise, can avoid learning Automata being stuck at a local optimum point. Our simulation shows that this proposed method can be improve the convergence rate of Automata-based algorithm. Manuscript profile

Open Access Article

24 - A Parallel Bacterial Foraging Optimization Algorithm implementation on GPU
A. Rafiee S. M. Mosavi

20.1001.1.16823745.1396.15.2.12.3

Bacterial foraging algorithm is one of the population-based optimization algorithms that used for solving many search problems in various branches of sciences. One of the issues discussed today is parallel implementation of population-based optimization algorithms on Gr More

Bacterial foraging algorithm is one of the population-based optimization algorithms that used for solving many search problems in various branches of sciences. One of the issues discussed today is parallel implementation of population-based optimization algorithms on Graphic Processor Units. Due to the low speed of bacterial foraging algorithm in the face of complex problem and also lack the ability to solve large-scale problems by this algorithm, Implementation on the graphics processor is a suitable solution to cover the weaknesses of this algorithm. In this paper, we proposed a parallel version of bacterial foraging algorithm which designed by CUDA and has ability to run on GPUs. The performance of this algorithm is evaluated by using a number of famous optimization problems in comparison with the standard bacterial foraging optimization algorithm. The results show that Parallel Algorithm is faster and more efficient than standard bacterial foraging optimization algorithm. Manuscript profile

Open Access Article

25 - Analyzing the Optimization Problem of Resource Allocation in SIP Proxies and Providing an Overload Control Algorithm with Max-min Fairness
M. Jahanbakhsh S. V. Azhari V. Ghasemkhani

20.1001.1.16823745.1396.15.3.14.7

Session Initiation Protocol (SIP) is an application layer protocol designed to create, manage, and terminate multimedia sessions in the IP multimedia subsystem (IMS). The widespread use of this protocol results in high traffic volume over SIP proxies, requiring delicate More

Session Initiation Protocol (SIP) is an application layer protocol designed to create, manage, and terminate multimedia sessions in the IP multimedia subsystem (IMS). The widespread use of this protocol results in high traffic volume over SIP proxies, requiring delicate CPU allocation to flows. In this paper, we analyze the optimization problem of resource allocation in SIP proxies with two objective functions: maximizing total throughput and minimizing the least squares. Maximizing total throughput, prioritizes intra-domain flows over inter-domain ones, as the latter pass through two intermediate proxies. On the other hand, minimizing the least squares corresponds to a max-min fairness policy. Hence, we use round robin scheduling in proxies. In addition, we propose a SIP overload control algorithm that limits re-transmissions and prevents instability of proxies by controlling the length of SIP message backlog for each flow. This algorithm leads to better use of processing resources, in comparison with existing overload control algorithms. Manuscript profile

Open Access Article

26 - Placement of AVRs and Reconfiguration of Distribution Networks Simultaneously and Robust Considering Load Uncertainty
M. R. Shakarami Y. Mohammadi Pour

20.1001.1.16823745.1396.15.4.1.6

: In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive More

: In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive power losses, reliability, voltage profile, voltage stability, and load capacity of lines (MVA). Various load levels were incorporated into the objective function to make sure that switch status in reconfiguration and AVR taps and locations would be robust against load variations. This paper also introduced a new method for calculating the load levels with respect to load uncertainty. It also considered all loads based on a voltage-dependent model. Several scenarios are defined to thoroughly assess the proposed approach. Integer particle swarm optimization algorithm (IPSO) was used to solve the mentioned optimization problem. The results obtained by the simulation of 33-bus and 69-bus standard IEEE .radial power distribution networks demonstrated the effectiveness of the proposed approach Manuscript profile

Open Access Article

27 - Transmission Expansion Planning in a Deregulated Power System Using Multiobjective Differential Evolution Algorithm
f. rashidi

20.1001.1.16823745.1396.15.4.2.7

Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requ More

Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requires more investment in constructing additional electrical transmission lines. Power system restructuring and deregulation has increased uncertainties in transmission expansion planning and made investment in electrical transmission lines more complicated and less appealing for private parties. This paper proposes a new approach for transmission line expansion planning in deregulated networks. To do that, a multi objective programming problem which consists of various objective functions such as minimizing capital investment for constructing new transmission lines, minimizing congestion in transmission lines and maximizing the investment from private parties is suggested such that access to competitive, economic and reliable energy market is facilitated. To solve the proposed multi objective optimization problem, the Pareto differential evolution algorithm is used. Applying this algorithm to the proposed multi objective programming problem generates set of optimal plans that shows the best compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified. Manuscript profile

Open Access Article

28 - Optimal Sitting and Sizing of Renewable Energy Sources and Charging Stations Simultaneously Based on Improved GA-PSO Algorithm
M. Rezaei Mozafar M. Rezaei Mozafar

20.1001.1.16823745.1396.15.4.3.8

Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, a More

Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, and voltage deviations. These challenges must be minimized by optimal planning based on the variable output from RES to meet the additional demand caused by EV charging. In this paper, a novel method for optimal locating and sizing of RES and EV charging stations simultaneously and managing vehicle charging process is provided. A multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost. In this optimization problem, the location and capacity of RES and EV charging stations are the objective variables. Coefficients which are dependent on wind speed, solar radiation, and hourly peak demand ratio for the management of the EV charging pattern in low load hours are introduced. GA-PSO hybrid improved optimization algorithm is used to solve the optimization problem in five different scenarios. The performance of the proposed method on IEEE 33-bus system has been investigated to validate the effectiveness of the novel GA-PSO method to optimal sitting and sizing of RES and EV charging stations simultaneously Manuscript profile

Open Access Article

29 - Optimal and Simultaneously Compensation of Active, and Reactive Powers in Power System Using of Plug in Electric Vehicle
f. rashidi H. Feshki Farahani

20.1001.1.16823745.1396.15.4.5.0

Plug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and propos More

Plug in electric vehicles besides environment pollution reduction can help power system operation. One of the most important capabilities of them is providing activeand reactive power. This paper considers grid constraints, technical concerns and market price and proposes a framework to allocate the PEV capacity such that operational cost paid by distribution system operator (DSO) to power provider of active and reactive power is minimized. For this purpose, an objective function is defined that includes the payment for each power provider. This objective function is minimized based on particle swarm optimization subject to grid and vehicles constraints. In this framework, the PEVs compete with generator to produce active and reactive power. In order to accelerate the optimization process and prevent the algorithm from being trapped in local optima, new heuristic approaches are included to the original PSO algorithm. To evaluate the effectiveness of the propose method, it is implemented on the low voltage with 134 customer and including the other power providers and the amount of each participants production and payment cost to each component is determined. Manuscript profile

Open Access Article

30 - Multi-Criteria Operation Optimization of Combined Cool, Heat and Power (CCHP) Generation Systems in a Microgrid
M. Setayeshnazar F. Amiri

20.1001.1.16823745.1396.15.4.9.4

Energy efficiency is one of the most important issues in the power system studies and many methods are used to improve power systems efficiency. Combined cool, heat and power (CCHP) systems are one of the most important technologies that can improve power system efficie More

Energy efficiency is one of the most important issues in the power system studies and many methods are used to improve power systems efficiency. Combined cool, heat and power (CCHP) systems are one of the most important technologies that can improve power system efficiency and these systems use their excess heat for supplying heat and cool loads. This paper presents a framework for optimal operation of CCHP systems in a microgrid. At first the unit cost functions are used to optimize operation of CCHP units. Then the algorithm determines the optimal operating strategy of microgrid units. A multi-criteria operation optimization method is proposed that uses primary energy consumption, pollution emissions and operating costs as criteria. The case study is performed for a nine bus microgrid and the results are compared with reference articles results and the advantage of the proposed method is investigated. Manuscript profile

Open Access Article

31 - Optimum Design of Out-runner PM BLDC Motor with High Torque Density for Flywheel Applications as Energy Storages: Design, FEA and Fabrication
O. Safdarzadeh H. Torkaman Mohammad Mahdavy Fakhr

20.1001.1.16823745.1397.16.1.4.0

Optimum design of electrical motors may be considered as a complex optimization problem due to the wide variety of mechanical, electrical, electromagnetics parameters, although recently it can be accomplished utilizing heuristic optimization algorithms. In this paper op More

Optimum design of electrical motors may be considered as a complex optimization problem due to the wide variety of mechanical, electrical, electromagnetics parameters, although recently it can be accomplished utilizing heuristic optimization algorithms. In this paper optimum design of an out-runner PM BLDC motor for flywheel energy storage applications is performed. The optimization utilized particle swarm optimization (PSO) algorithm to achieve maximum torque density. Accordingly, the motor design equations are employed in the fitness function of the algorithm. Based on the random initial values and respecting the designs constraints, the optimum design is achieved. Effectiveness of the algorithm results are verified by finite element analysis (FEA) and motor operating parameters are obtained and analyzed. Finally, the prototype of the motor is fabricated and experimental results are demonstrated to show the applicability of the model and analysis. Manuscript profile

Open Access Article

32 - Power Control and Subchannel Allocation in OFDMA Macrocell-Femtocells Networks
H. Davoudi M. Rasti

20.1001.1.16823745.1397.16.2.3.1

Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. More

Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. Since macrocell users have priority to femtocell ones, presence of femtocell users should not prevent macrocell users to access minimum quality-of-service. In this paper, a power control and subchannel allocation scheme in downlink transmission an orthogonal frequency division multiple access (OFDMA) based two tier of macrocell and femtocell is proposed, aiming the maximization of femtocell users total data rate, in which the minimum QOS for all macrocell users and femtocell delay-sensitive users is observed. In macrocell tier, two different problems are considered. The first problem aim to maximizing the total threshold of tolerable cross-tier interference for macrocell users and the second problem’s goal is minimizing the macrocell’s total transmission power. For the femtocell tier, maximizing the users total data rate is the objective. Hungrian method, an assignment optimization method, is used for solving the first problem in macrocell tier. Moreover, in order to solve the second problem a heuristic method for subchannel allocation is proposed and dual Lagrange method is used for power control. In addition, in order to solve the problem for femtocell tier, a heuristic method is used for subchannel allocation. Subsequently, a dual Lagrange method which is one of the convex optimization problem solver is used, so that we can control the power. Finally, an extend simulations are performed to validate the performance of the proposed method. Manuscript profile

Open Access Article

33 - Hyper Spherical Search Optimization Algorithm Based on Chaos Theory
Mohammad Kalantari S. Sohrabi H. Rashidy Kanan H. Karami

20.1001.1.16823745.1397.16.2.4.2

A Hyper Spherical Search (HSS) optimization algorithm based on chaos theory is proposed that resolves the weakness of the standard HSS optimization algorithm including the speed of convergence and the sequential increment in the number of algorithm iterations to achieve More

A Hyper Spherical Search (HSS) optimization algorithm based on chaos theory is proposed that resolves the weakness of the standard HSS optimization algorithm including the speed of convergence and the sequential increment in the number of algorithm iterations to achieve the optimal solution. For this, in the particle initiation and search steps of the proposed algorithm, random values used in the standard algorithm are replaced with the values of two mappings, Chebyshev and Liebovitch, that makes the results of the proposed algorithm definite and decreases their standard deviation. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate search algorithm to find the optimal solution in comparison to standard hyper spherical search algorithm and some other optimization algorithms such as genetic, particle swarm, and harmony search algorithm. Manuscript profile

Open Access Article

34 - Cell Association Combined with Interference Management in Heterogeneous Cellular Networks Using a Distributed Algorithm
Maryam Chinipardaz Seyed Majid Noorhosseini

20.1001.1.16823745.1397.16.3.1.1

Due to the growing demand of cellular networks, the need to increase the capacity of these networks has always been a challenge. Heterogeneous cellular networks using small base stations alongside macro base stations are low cost and effective solutions for this problem More

Due to the growing demand of cellular networks, the need to increase the capacity of these networks has always been a challenge. Heterogeneous cellular networks using small base stations alongside macro base stations are low cost and effective solutions for this problem. However the differences between the various BSs in heterogeneous networks have created new challenges in terms of cell association and interference management compared with the traditional cellular networks. Therefore, the design of new and efficient methods for allocating cells and resources in these networks is an open research topic. This paper addresses the need for an efficient solution to simultaneously allocating cells and subbands in order to prevent interference for all users. The protocol interference model and its modeling methods in cellular networks have been studied. After modeling the system, the problem is formulated as an integer optimization problem. Then, by reformulating the problem and using a one-level dual decomposition, an algorithm with efficient complexity with near-optimal answers is attained. Thereafter, a distributed protocol is presented in which each user and each base station would only require local information for making decisions. The simulation results confirm the effectiveness of the proposed solution. Manuscript profile

Open Access Article

35 - Grayscale Images Deblurring Using Robust Optimization Problem in Uncertainty Conditions in Blurring Model Parameters
Zeinab Mohammadi Ebrahim Daneshifar Abbas Ebrahimi moghadam M. Khademi

20.1001.1.16823745.1399.18.4.10.6

Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has oc More

Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has occurred is assumed unknown, while in non-blind de-blurring, this kernel is given. In blind de-blurring, the blurring kernel must be estimated in order to sharpen the corrupted image. This may increase the computational cost of the de-blurring process. Non-blind image de-blurring is an ill-posed problem with linear reverse issues. Therefore, we develop optimization problems in order to estimate the original sharp images. Usually, non-blind de-blurring methods assume that the blurring kernel is error-free, however, in practice our knowledge of the PSF is uncertain. Hence, in this paper, we use a semi-blind method for de-blurring the blurred image that is robust to this uncertainty. The proposed robust optimization model is followed by a filter for image de-blurring that can attain the solution with lowest possible error in the worst case scenarios, that is, the maximum uncertainty about the blurring kernel. Based on the simulation results, our proposed semi-blind model yields more than 4 dB PSNR improvements compared to conventional blind image de-blurring methods. Manuscript profile

Open Access Article

36 - Improved Realization of Controlled Unitary Gates in the One-Way Quantum Computation Model Using the Extended Measurement Calculus
M. Houshmand M. hooshmand

20.1001.1.16823745.1398.17.1.12.5

In one-way quantum computation model (1WQC), the quantum correlations in an entangled state, called a cluster state or graph state, are used to perform universal quantum computations using single-qubit measurements. In 1WQC, the computations are shown by measurement pat More

In one-way quantum computation model (1WQC), the quantum correlations in an entangled state, called a cluster state or graph state, are used to perform universal quantum computations using single-qubit measurements. In 1WQC, the computations are shown by measurement patterns or simply patterns. The synthesis problem in the 1WQC model is defined as extracting the pattern from a given arbitrary unitary matrix. The important criteria in evaluating measurement patterns in the 1WQC model, are the size, the depth and the number of entanglements of the pattern. In this paper, a new approach is proposed to synthesize controlled-unitary U gates where U is a single-qubit gate. To this end, for the first time, the idea of applying the extended measurement calculus, which utilizes the measurements in different Bloch sphere planes, is used in the synthesis of the 1WQC model. Some optimizations are proposed for this method and a new approach is presented to synthesize controlled-U gates for the 1WQC model which improves the evaluation criteria of size, depth and the number of entanglements in this model as compared to the best previous result by 9.1%, 30% and 18.1%, respectively. Manuscript profile

Open Access Article

37 - Optimal Design of Six-Phase Radial Flux Permanent Magnet Synchronous Generator for Small Scale Wind Turbine Applications
M. E. Moazzen S. A. Gholamian

20.1001.1.16823745.1398.17.2.3.8

This paper presents optimal design of a six-phase permanent magnet synchronous generator (PMSG) for use in direct drive wind turbines. High Dimensions and manufacturing cost and low efficiency are the disadvantages of generators connected to wind turbines without gearbo More

This paper presents optimal design of a six-phase permanent magnet synchronous generator (PMSG) for use in direct drive wind turbines. High Dimensions and manufacturing cost and low efficiency are the disadvantages of generators connected to wind turbines without gearbox because of their low nominal speed. Therefore, the main purpose of this paper is to optimize the design of the PMSG based on the reduction of losses and the construction cost of the generator. For this purpose, the relations governing the design of the radial flux PMSG have been introduced and then a design algorithm has been extracted. Subsequently, by defining a multi-objective optimization problem and using the particle swarm optimization (PSO) algorithm, the optimum design variables are determined in a suitable range and the minimum losses and construction cost of the generator are obtained. The optimal design has been verified by using finite element analysis. Manuscript profile

Open Access Article

38 - Improved BIRCH Clustering by Chemical Reaction Optimization Algorithm to Health Fraud Detection
M. Abdolrazzagh-Nezhad M. Kherad

20.1001.1.16823745.1398.17.2.15.0

With regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are seve More

With regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are several fraud detection algorithms. In the paper, the BIRCH clustering algorithm, as one hierarchical clustering algorithm, is hybridized with a chemical reaction optimization algorithm (CRO). The BIRCH with linear time complexity is able for clustering large scale data and identifying their noises and the CRO, as one of new meta-heuristic algorithm inspired by the chemical reactions in the real world, explores the search space with a dynamic population size based on four reactions such as on-wall ineffective collision, decomposition, inter-molecular ineffective collision and synthesis. Due to the improved BIRCH-CRO removes the internal clustering process of the classic BIRCH and determines the optimal values of its main parameters, it causes that the computational time decreases and accuracy and precision of detecting fraud data increase since its experimental results is compared with the exist unsupervised algorithms. Also, the proposed fraud detection algorithm has the ability to perform on online data and large scale data, and given the obtained results, it provides a proper performance. Manuscript profile

Open Access Article

39 - Scheduling of Scientific Workflow Applications in Multi-Cloud Environment Using Cuckoo Search Algorithm
S. Mohammad Latif PourKarimi Somayeh Abdi

20.1001.1.16823745.1399.18.4.11.7

Multi-cloud environments consist of the considerable variety of resources where the cost of scheduling workflow applications can be significantly reduced in such environments and the resource limitationsimposed by commercial cloud providers can bealso overcome. Accordin More

Multi-cloud environments consist of the considerable variety of resources where the cost of scheduling workflow applications can be significantly reduced in such environments and the resource limitationsimposed by commercial cloud providers can bealso overcome. Accordingly, this study addresses the scheduling of scientific workflowapplications in a multi-cloud environment under a deadline with the aim of minimizing costs. In this paper,an algorithm for scheduling of workflow applications in multi-cloud environment is presented using the cuckoo search algorithm which is one of the most popular meta-heuristic methods. The Cuckoo Search Algorithm is able to search the solution space in a short time and find solutions in the vicinity of the optimal global solution that is close to it. The results show that the proposed approach of this research has better performance in comparison with other meta- heuristic approach in terms of cost reduction. Moreover, the obtained solutions of the proposed meta- heuristic algorithm are in a desirable degree close to the global optimal solutions of mathematical model. Manuscript profile

Open Access Article

40 - A Task Scheduling and Mapping Approach to Enhance the Main Design Challenges of Multiprocessor Systems on Chip
حمیدرضا زرندی

20.1001.1.16823745.1399.18.3.12.6

In this paper, a static task scheduling and mapping heuristic approach to optimize execution time, reliability, power and temperature of multiprocessor systems on chip is presented. This method is proposed based on the list scheduling approach and utilized task replicat More

In this paper, a static task scheduling and mapping heuristic approach to optimize execution time, reliability, power and temperature of multiprocessor systems on chip is presented. This method is proposed based on the list scheduling approach and utilized task replication, dynamic voltage and frequency scaling, and adding cooling slacks to improve reliability, power consumption and temperature to expand the design space and explore the solution set more efficiently. Due to the existing trade-offs among the considered parameters and their optimization, the optimization process is complicated and our proposed method is used the Pareto front generation technique. Moreover, our proposed method, models the objectives comprehensively to consider their dependency. Several experiments are performed to demonstrate the performance and capability of the proposed method in joint optimization of the parameters and extracting the proper solution set. Compared to the previous research, our proposed method outperforms them in optimizing the considered design parameters and its results is 19% better averagely than an efficient studied heuristic method. Manuscript profile

Open Access Article

41 - Proposing an Intelligent Method for Design and Optimization of Double tail Comparator
Sadegh Mohammadi-Esfahrood Seyed-Hamid Zahiri

20.1001.1.16823745.1399.18.3.13.7

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradi More

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradicting design purposes, circuit constraints and necessities, design of comparators and obtaining best circuit performance are complicated and challenging. Such challenges in circuit design necessitate presenting approaches which not only satisfy all the objectives but also, they are cost effective in terms of time and cost. One of the approaches which has recently attracted attentions is the heuristic algorithms based intelligent Methods. Inclined Planes system Optimization algorithm (IPO) is a novel heuristic algorithm inspired by dynamic movement of the objects on frictionless inclined planes. But despite its remarkable ability for exploration and exploitation of the search space, its standard model has complex relationships with many structural parameters that often confuse the user in choosing the effective values for them. In this paper, IPO algorithm is simplified to present a heuristic algorithm (called SIPO) and its efficiency in optimization of 10 standard benchmarks has been evaluated. Then, a multi-objective version of the proposed algorithm (called MOSIPO) for design and optimization of double tail comparator is presented and its efficiency in optimization of double tail comparator has been evaluated and compared with popular multi-objective intelligent methods. The results clearly demonstrate the improved performance and superiority of SIPO and MOSIPO compared to the other methods. Manuscript profile

Open Access Article

42 - Quality of Service Aware Service Composition Method Using Biogeography-Based Optimization (BBO) Algorithm
S. Saligheh B. Arasteh

20.1001.1.16823745.1398.17.3.10.7

Fast development in the utilization of cloud computing leads to publishing more cloud services on the cloud environment. The single and simple services cannot satisfy the users’ real-world complex requirements. To create a complex service, it is necessary to select and More

Fast development in the utilization of cloud computing leads to publishing more cloud services on the cloud environment. The single and simple services cannot satisfy the users’ real-world complex requirements. To create a complex service, it is necessary to select and compose a set of simple services. Therefore, it is essential to embed a service composition system in cloud computing environment. Service composition is one of the important NP-hard problems in the service-oriented computings. In this paper, a biogeography-based optimization algorithm is used to create the optimal composite-services. The proposed method was simulated and executed on five different scenarios with different number of tasks and candidate services. The throughput of the proposed method, genetic algorithm and particle swarm optimization algorithm are respectively 0.9997, 0.9975 and 0.9994; furthermore, the reliability of these methods are respectively 0.9993, 0.9980 and 0.9982. The results of simulations indicate that the proposed method outperforms the previous methods in the same conditions in terms of throughput, successability, reliability, response time, and stability. Manuscript profile

Open Access Article

43 - Optimal Resource Allocation in Multi-Task Software-Defined Sensor Networks
S. A. Mostafavi M. Agha Sarram T. Salimian

20.1001.1.16823745.1398.17.4.13.2

Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program wh More

Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program which serves as a common communication infrastructure for several different applications. Different sensor applications in the network can have different target functions and decision parameters. Due to the resource constraints of sensor network nodes, the multiplicity and variety of tasks in each application, requirements for different levels of quality of service, and the different target functions for different applications, the problem of allocating resources to the tasks on the sensors is complicated. In this paper, we formulate the problem of allocating resources to the sensors in the SDSN with different objective functions as a multi-objective optimization problem and provide an effective solution to solve it. Manuscript profile

Open Access Article

44 - DRSS-Based Localization Using Convex Optimization in Wireless Sensor Networks
Hassan Nazari M. R. Danaee M. Sepahvand

20.1001.1.16823745.1398.17.4.14.3

Localization with differential received signal strength measurement in recent years has been very much considered. Due to the fact that the probability density function is known for given observations, the maximum likelihood estimator is used. This estimator can be asym More

Localization with differential received signal strength measurement in recent years has been very much considered. Due to the fact that the probability density function is known for given observations, the maximum likelihood estimator is used. This estimator can be asymptotically represented the optimal estimation of the location. After the formation of this estimator, it is observed that the corresponding cost function is highly nonlinear and non-convex and has a lot of minima, so there is no possibility of achieving the global minimum with Newton method and the localization error will be high. There is no analytical solution for this cost function. To overcome this problem, two methods are existed. First, the cost function is approximated by a linear estimator. But this estimator has poor accuracy. The second method is to replace the non-convex cost function with a convex one with the aid of convex optimization methods, in which case the global minimum is obtained. In this paper, we proposed new convex estimator to solve cost function of maximum likelihood estimator. The results of the simulations show that the proposed estimator has up to 20 percent performance improvement compared with existing estimators, moreover, the execution time of proposed estimator is 30 percent faster than other convex estimators. Manuscript profile

Open Access Article

45 - An Intelligent Approach for OFDM Channel Estimation Using Gravitational Search Algorithm
F. Salehi mohammad hassan majidi N. Neda

20.1001.1.16823745.1399.18.1.8.8

The abundant benefits of Orthogonal Frequency-Division Multiplexing (OFDM) and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system’s efficiency is the accurate estim More

The abundant benefits of Orthogonal Frequency-Division Multiplexing (OFDM) and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system’s efficiency is the accurate estimation of channel state information (CSI). In the literatures many techniques have been studied in order to estimate the CSI. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response (CFR) properly only using the received signals. Unfortunately each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using gravitational search algorithm (GSA). This method can achieve accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate our proposed method is capable of providing the same performance as GA and PSO. For a two-path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate (BER) and the mean square error (MSE). Manuscript profile

Open Access Article

46 - Performance Improvement of Polynomial Neural Network Classifier using Whale Optimization Algorithm
Mahsa Memari A. Harifi a. Khalili

20.1001.1.16823745.1401.20.2.1.7

Polynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, le More

Polynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, leads to more computation time and more storage space requirement. In general, it can be said that the architecture of the polynomial neural networks is very complex and it requires large memory and computation time. In this research, a novel approach has been proposed to improve the classification performance of a polynomial neural network using the Whale Optimization Algorithm (PNN-WOA). In this approach, the PDs are generated at the first layer based on the combination of two features. The second layer nodes consists of PDs generated in the first layer, input variables and bias. Finally, the polynomial neural network output is obtained by sum of weighted values of the second layer outputs. Using the Whale Optimization Algorithm (WOA), the best vector of weighting coefficients will be obtained in such a way that the PNN network reach to the highest classification accuracy. Eleven different dataset from UCI database has been used as input data of proposed PNN-WOA and the results has been presented. The proposed method outperforms state-of-the-art approaches such as PNN-RCGA, PNN-MOPPSO, RCPNN-PSO and S-TWSVM in most cases. For datasets, an improvement of accuracy between 0.18% and 10.33% can be seen. Also, the results of the Friedman test indicate the statistical superiority of the proposed PNN-WOA model compared to other methods with p value of 0.039. Manuscript profile

Open Access Article

47 - Optimal Placement of Energy Storage Systems Taking into Account the Uncertainties of Renewable Energy Generation, Load and Electricity Prices
NAVID TAGHIZADEGAN KALANTARI Yousef Fonooni Morteza Ahangari Hassas

20.1001.1.16823745.1400.19.3.11.2

One of the main goals of distribution network operators is to reduce the cost of operating the network and improve profit. In this paper, the problem of siting and determining the size of energy storage batteries are studied. The constituent components of the objective More

One of the main goals of distribution network operators is to reduce the cost of operating the network and improve profit. In this paper, the problem of siting and determining the size of energy storage batteries are studied. The constituent components of the objective function of the placement problem include the profit from the operation of the distributed generation unit, the profit from the reduction of grid power losses, the cost of installing an energy storage system, and the profit from the reduction of energy purchased from the upstream network. The model used for positioning is based on the probabilistic behavior of solar radiation, energy consumers, and electricity market operators. To model the stochastic nature of the output power of solar power plants, the probability density function has been used, and to model the load and price of electricity, the scenario method has been used. The simulations were performed using MATLAB software. The proposed method can manage the generation of solar power plants using the siting and management of charge and discharge of batteries. Manuscript profile

Open Access Article

48 - A New Data Clustering Method Using 4-Gray Wolf Algorithm
Laleh Ajami Bakhtiarvand Zahra Beheshti

20.1001.1.16823745.1400.19.4.12.5

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in More

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets. Manuscript profile

Open Access Article

49 - Energy-Aware Data Gathering in Rechargeable Wireless Sensor Networks Using Particle Swarm Optimization Algorithm
Vahideh Farahani Leili Farzinvash Mina Zolfy Lighvan Rahim Abri Lighvan

20.1001.1.16823745.1400.19.4.13.6

This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine t More

This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware Data Gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5.6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero Manuscript profile

Open Access Article

50 - Novel AI-Based Metaheuristic Optimization Approaches for Designing INS Navigation Systems
علی محمدی Farid Sheikholeslam Mehdi Emami

20.1001.1.16823745.1401.20.1.15.9

Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantag More

Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantage of novel approaches of intelligent metaheuristic optimization for designing integrated navigation systems. For this purpose, the inclined planes system optimization algorithm with several modified and new versions have been used along with two well-known methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS inertia measurement modules. Process and measurement noise covariance matrices are considered as design variables and the sum of mean-squares-error as an objective function in the form of a single-objective minimization problem. Outputs are presented in terms of statistical and performance indicators such as runtime, fitness, convergences, angular-velocity accuracy, latitude, longitude, altitude, roll, pitch, yaw, and routing along with the ranking of algorithms. The overall assessment indicated the correctness of the performance and the relative superiority of the IPO and IIPO over the competitors and competitive performance of the assumed algorithms in comparison with the volume of considerations and calculations of the base problem. Manuscript profile

Open Access Article

51 - Introducing Intelligent Mutation Method Based on PSO Algorithm to Solve the Feature Selection Problem
Mahmoud Parandeh Mina Zolfy Lighvan jafar tanha

20.1001.1.16823745.1401.20.3.4.2

Today, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection alg More

Today, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection algorithms are used to improve efficiency and reduce the computational cost of machine learning algorithms. A variety of methods for selecting features are provided. Among the feature selection methods are evolutionary algorithms that have been considered because of their global optimization power. Many evolutionary algorithms have been proposed to solve the feature selection problem, most of which have focused on the target space. The problem space can also provide vital information for solving the feature selection problem. Since evolutionary algorithms suffer from the pain of not leaving the local optimal point, it is necessary to provide an effective mechanism for leaving the local optimal point. This paper uses the PSO evolutionary algorithm with a multi-objective function. In the proposed algorithm, a new mutation method that uses the particle feature score is proposed along with elitism to exit the local optimal points. The proposed algorithm is tested on different datasets and examined with existing algorithms. The simulation results show that the proposed method has an error reduction of 20%, 11%, 85%, and 7% in the Isolet, Musk, Madelon, and Arrhythmia datasets, respectively, compared to the new RFPSOFS method. Manuscript profile

Open Access Article

52 - Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm
َAzar Rafiei Parham Moradi Abdolbaghi Ghaderzadeh

20.1001.1.16823745.1401.20.4.7.7

Multi-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in mul More

Multi-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in multi-label problems. Multi-label feature selection is one of the NP solutions, and so far, a number of solutions based on collective intelligence and evolutionary algorithms have been proposed for it. Increasing the dimensions of the problem leads to an increase in the search space and consequently to a decrease in efficiency and also a decrease in the speed of convergence of these algorithms. In this paper, a hybrid collective intelligence solution based on a binary particle swarm optimization algorithm and local search strategy for multi-label feature selection is presented. To increase the speed of convergence, in the local search strategy, the features are divided into two categories based on the degree of extension and the degree of connection with the output of the problem. The first category consists of features that are very similar to the problem class and less similar to other features, and the second category is similar features and less related. Therefore, a local operator is added to the particle swarm optimization algorithm, which leads to the reduction of irrelevant features and extensions of each solution. Applying this operator leads to an increase in the convergence speed of the proposed algorithm compared to other algorithms presented in this field. The performance of the proposed method has been compared with the most well-known feature selection methods on different datasets. The results of the experiments showed that the proposed method has a good performance in terms of accuracy. Manuscript profile

Open Access Article

53 - Multi-Objective Logic Synthesis of Quantum Circuits
Arezoo Rajaei Mahboobeh Houshmand Seyyed Abed Hosseini

20.1001.1.16823745.1401.20.3.6.4

Quantum computing is a new method of information processing that is based on the concepts of quantum mechanics and leads to strange and powerful events in the quantum field. The logic synthesis of quantum circuits refers to the process of converting a given quantum gate More

Quantum computing is a new method of information processing that is based on the concepts of quantum mechanics and leads to strange and powerful events in the quantum field. The logic synthesis of quantum circuits refers to the process of converting a given quantum gate into a set of gates that can be implemented in quantum technologies. The most famous logic synthesis methods are CSD and QSD. The main goal of this study is to present a multi-objective logical synthesis method combining the above two methods in the quantum circuit model with the aim of optimizing the evaluation criteria. In this proposed method, the solution space is created from different combinations of CSD and QSD decomposition methods. The created solution space is a space with a very large exponential size. Then, using a bottom-up approach of multi-objective dynamic programming, a method is presented to search only a part of the entire solution space to find circuits with the optimal Pareto costs. The obtained results show that this method creates a balance between the evaluation criteria and produces many optimal Pareto solutions that can be selected according to different quantum technologies. Manuscript profile

Open Access Article

54 - Reactive Power management in Distribution Network Considering uncertainties in the Presence of Discrete and Continuous Reactive Power Compensator Equipment
mahboobeh etemadizadeh maryam Ramezani H. Falaghi

20.1001.1.16823745.1402.21.2.5.8

The increasing rate of distributed generation resources expansion into power systems and the random nature of these resources have altered the operation and design of these networks, and reactive power management in distribution networks belongs to this category. The us More

The increasing rate of distributed generation resources expansion into power systems and the random nature of these resources have altered the operation and design of these networks, and reactive power management in distribution networks belongs to this category. The use of these resources in distribution networks is not without challenges and the lack of optimal management of reactive power may not bring economic efficiency for the network. Energy storage systems have the potential to solve this problem. Therefore, in this article, reactive power management in a microgrid connected to the main grid, taking into account distributed generation sources, energy storage systems and discrete reactive power compensating equipment, including capacitor banks, taking into account uncertainty in network load and Wind and solar power generation has been done. Finally, the efficiency of the method is demonstrated by numerical examinations on the distribution networks of 33 and 69 IEEE buses and in the GAMS optimization software. Manuscript profile

Open Access Article

55 - Identification of Transfer Function Parameters of Brushless DC Motor Using Particle Swarm Algorithm
Ahmad Shirzadi Arash Dehestani Kolagar Mohammad Reza Alizadeh Pahlavani

20.1001.1.16823745.1402.21.2.6.9

So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is ess More

So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem. Manuscript profile

Open Access Article

56 - Multi-Objective Economic-Environment Scheduling of Microgrids in the Presence of Hybrid Electric Vehicles and Demand Response to Smooth the Distribution Nodal Prices
ali mirzaei NAVID TAGHIZADEGAN KALANTARI Sajad Najafi Ravadanegh

Today, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper int More

Today, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper introduces a new multi-objective optimization model, which in the first objective, minimizes the total operation cost of the microgrid, and in the second objective, improves the reliability index by reducing the amount of energy not supplied. Due to these two objectives, a multi-objective evolutionary seagull optimization algorithm is used to find the optimal global solutions. In this regard, hybrid electric vehicles and demand response programs are used to smooth out distribution nodal prices and reduce CO2 emissions. The 69-bus distribution network has been used to evaluate the efficiency of the proposed method. Manuscript profile

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