An adaptive strategy based multi-population multi-objective optimization algorithm

被引:1
作者
Zhao, Tianhao [1 ]
Wu, Linjie [1 ]
Cui, Zhihua [1 ]
Qin, A. K. [2 ]
机构
[1] Taiyuan Univ Sci & Technol, Shanxi Key Lab Big Data Anal & Parallel Comp, Taiyuan, Peoples R China
[2] Swinburne Univ Technol, Dept Comp Technol, Hawthorn, Vic 3122, Australia
基金
中国国家自然科学基金;
关键词
Evolutionary algorithm; Multi-population; Adaptive strategy; Multi-objective optimization; GENETIC ALGORITHM;
D O I
10.1016/j.ins.2024.120913
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An algorithm is sensitive to parameters; different parameter settings for solving optimization problems can thus have a serious impact on algorithm performance. This leads to an inability to determine the optimal set of parameters for the algorithm to solve the problem at hand. In this study, we propose an adaptive strategy with a multi-population multi-objective algorithm (A-MPMO) framework to select the appropriate set of genetic settings according to the problem to be solved and eliminate the sensitivity of the algorithm to the parameters. Multi-population are often combined with Evolutionary Algorithms (EAs) as an effective strategy to maintain population diversity. First, we divided the population generated by the algorithm into multiple subpopulations to expand the search range and updated them iteratively using operators with different genetic parameters. Second, based on multi-population, subpopulations compete for limited computational resources, implying that the size of each subpopulation adaptively adjusts according to the degree of its contribution to problem solving. Finally, a set of subpopulations that are best suited to solve the problem is selected to improve the adaptability to different problems. For DTLZ, ZDT, and UF, compared to the other algorithms, A-MPMO was experimentally shown to produce better performance.
引用
收藏
页数:17
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