Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a nov...

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Vydané v:Computational intelligence and neuroscience Ročník 2020; číslo 2020; s. 1 - 26
Hlavní autori: Zhang, Maosheng, Wang, Yi, Chen, Li, Yang, Wusi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Predmet:
Algorithms
Comparative analysis
Competition
Computer Simulation
Convergence
Evolutionary algorithms
Mathematical optimization
Multiple objective analysis
Optimization algorithms
Particle swarm optimization
ISSN:1687-5265, 1687-5273, 1687-5273
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Shrnutí:The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Daniele Bibbo
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2020/5132803