Welcome to the InfoSci Platform
A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism

A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism

Chen Yan, Cai Mengxiang, Zheng Mingyong, Li Kangshun
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 17
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781683180197|DOI: 10.4018/IJCINI.301203
Cite Article Cite Article

MLA

Yan, Chen, et al. "A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism." IJCINI vol.16, no.1 2022: pp.1-17. http://doi.org/10.4018/IJCINI.301203

APA

Yan, C., Mengxiang, C., Mingyong, Z., & Kangshun, L. (2022). A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 16(1), 1-17. http://doi.org/10.4018/IJCINI.301203

Chicago

Yan, Chen, et al. "A Many-Objective Practical Swarm Optimization Based on Mixture Uniform Design and Game Mechanism," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 16, no.1: 1-17. http://doi.org/10.4018/IJCINI.301203

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In recent years, multi-objective optimization algorithms, especially many-objective optimization algorithms, have developed rapidly and effectively.Among them, the algorithm based on particle swarm optimization has the characteristics of simple principle, few parameters and easy implementation. However, these algorithms still have some shortcomings, but also face the problems of falling into the local optimal solution, slow convergence speed and so on. In order to solve these problems, this paper proposes an algorithm called MUD-GMOPSO, A Many-Objective Practical Swarm Optimization based on Mixture Uniform Design and Game mechanism. In this paper, the two improved methods are combined, and the convergence speed, accuracy and robustness of the algorithm are greatly improved. In addition, the experimental results show that the algorithm has better performance than the four latest multi-objective or high-dimensional multi-objective optimization algorithms on three widely used benchmarks: DTLZ, WFG and MAF.