SIA OpenIR  > 智能检测与装备研究室
Alternative TitleResearch on Searching Performance Optimization of Multi-Objective Optimization Algorithm
李想1,2; 杜劲松1
Source Publication计算机仿真
Contribution Rank1
Keyword约束多目标优化 人工免疫系统 多智能体系统 多样性 收敛性


Other Abstract

In constrained multi-objective optimization problems, constraint conditions make them difficult for optimization algorithms to converge to the optimal solution or keep individual diversity. To improve the diversity and convergence of algorithms,a constrained multi-objective optimization algorithm combining artificial immune system with multi-agent system is proposed. The proposed algorithm combines the advantages of the artificial immune system and the multi-agent system,and completes the local and global search efficiently through neighborhood clone selection operator, neighborhood competition operator, neighborhood collaboration operator, and self-learning operator. The algorithm uses distance value and penalty to modify the objective values of agent individuals. During the evolutionary process,the algorithm utilizes the infeasible solutions with smaller constrained violation values to keep individual diversity and avoid prematurity. On the standard test functions ( CTP series) ,the proposed algorithm is compared with another three excellent algorithms. Experimental results show that the optimal solutions of the proposed algorithm are better than those of another three algorithms in terms of diversity and convergence,and the searching performance is optimized.

Document Type期刊论文
Corresponding Author李想
Recommended Citation
GB/T 7714
李想,杜劲松. 关于多目标优化算法搜索性能优化研究[J]. 计算机仿真,2018,35(9):271-276.
APA 李想,&杜劲松.(2018).关于多目标优化算法搜索性能优化研究.计算机仿真,35(9),271-276.
MLA 李想,et al."关于多目标优化算法搜索性能优化研究".计算机仿真 35.9(2018):271-276.
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