الگوریتم‌های فراابتکاری

List of Articles الگوریتم‌های فراابتکاری

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1 - 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
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2 - Stochastic Planning of Resilience Enhancement for Electric Power Distribution Systems against Extreme Dust Storms
M. Haghshenas R. Hooshmand M. Gholipour
20.1001.1.16823745.1401.20.2.12.8

Resilience in power systems refers to the system's ability to withstand severe disturbances with a low probability of occurrence. Because in recent years extreme dust storms have caused severe damage to Iran's electricity industry, especially in the south and southwest, More

Resilience in power systems refers to the system's ability to withstand severe disturbances with a low probability of occurrence. Because in recent years extreme dust storms have caused severe damage to Iran's electricity industry, especially in the south and southwest, in this paper proposed a new scenario-based stochastic planning model for enhancement of power distribution systems resilience against extreme dust storms. In proposed model, in the first stage, the investment costs to improve the distribution system resilience against extreme dust storms are minimized due to the financial constraints, and in the second stage, the expected operating costs in dust storm conditions are minimized due to the operating constraints. Because network's insulation equipment are major cause of distribution system vulnerabilities in the dust storms, measures in the planning stage include replacement of porcelain insulators with silicon-rubber type, installation of automatic tie switches and installation of emergency generators. In the second stage, the measures are divided into preventive actions and corrective actions, and coordination between stages 1 and 2 is implemented in such a way that the results of each stage depend on the decision variables of the other stage. The simulation results for IEEE 33-bus test system and a 209 bus radial distribution network located in Khuzestan province, Iran, confirm the efficiency of the proposed model in different financial conditions. Manuscript profile
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3 - 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

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List of Articles الگوریتم‌های فراابتکاری