A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation
Joint Authors
Wang, Jincheng
Yu, Hongli
Gao, Yuelin
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-23, 23 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-01
Country of Publication
Egypt
No. of Pages
23
Main Subjects
Abstract EN
In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed.
In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives.
In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity.
The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.
American Psychological Association (APA)
Yu, Hongli& Gao, Yuelin& Wang, Jincheng. 2020. A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation. Complexity،Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1142659
Modern Language Association (MLA)
Yu, Hongli…[et al.]. A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation. Complexity No. 2020 (2020), pp.1-23.
https://search.emarefa.net/detail/BIM-1142659
American Medical Association (AMA)
Yu, Hongli& Gao, Yuelin& Wang, Jincheng. A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation. Complexity. 2020. Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1142659
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142659