Particle swarm optimization

Open Access Article

1 - 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

2 - 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

3 - 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

4 - 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

5 - 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

6 - 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

7 - 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

8 - 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

9 - 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

10 - 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

11 - 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

12 - 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

13 - 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

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