%0 Journal Article
%T Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism
%A Chen
%A Li
%A Wang
%A Yi
%A Yang
%A Wusi
%A Zhang
%A Maosheng
%J -
%D 2020
%R https://doi.org/10.1155/2020/5132803
%X 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 -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
%U https://www.hindawi.com/journals/cin/2020/5132803/