Published in: مجله هوش مصنوعی و داده کاوی، دوره: 4، شماره: 1
COI code: JR_JADM-4-1_008
Paper Language: English
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Authors PSO for multi-objective problems: Criteria for leader selection and uniformity distributionH. Motameni - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
Abstract:This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems.
Keywords:Multi-objective optimization, Particle Swarm Optimization, Intensity Distance, Mutation
COI code: JR_JADM-4-1_008
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Motameni, H., 2016, PSO for multi-objective problems: Criteria for leader selection and uniformity distribution, Journal of Artificial Intelligence & Data Mining 4 (1), https://www.civilica.com/Paper-JR_JADM-JR_JADM-4-1_008.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Motameni, H., 2016)
Second and more: (Motameni, 2016)
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The University/Research Center Information:
Type: Azad University
Paper No.: 3721
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