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Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

Weiwei Yu, Li Zhang, Chengwang Xie
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 23
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa29
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MLA

Yu, Weiwei, et al. "Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies." IJCINI vol.15, no.4 2021: pp.1-23. http://doi.org/10.4018/IJCINI.20211001.oa29

APA

Yu, W., Zhang, L., & Xie, C. (2021). Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-23. http://doi.org/10.4018/IJCINI.20211001.oa29

Chicago

Yu, Weiwei, Li Zhang, and Chengwang Xie. "Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-23. http://doi.org/10.4018/IJCINI.20211001.oa29

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Abstract

Many-objective optimization problems (MaOPs) refer to those multi-objective problems (MOPs) withmore than three objectives. In order to solve MaOPs, a multi-objective particle swarm optimization algorithm based on new fitness assignment and multi cooperation strategy(FAMSHMPSO) is proposed. Firstly, this paper proposes a new fitness allocation method based on fuzzy information theory to enhance the convergence of the algorithm. Then a new multi criteria mutation strategy is introduced to disturb the population and improve the diversity of the algorithm. Finally, the external files are maintained by the three-point shortest path method, which improves the quality of the solution. The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation and IGD+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms. The experimental results show that FAMSHMPSO algorithm has obvious performance advantages in convergence, diversity and robustness.