数学优化
水准点(测量)
计算机科学
人口
最优化问题
差异进化
多目标优化
选择(遗传算法)
航程(航空)
突变
偏爱
进化算法
操作员(生物学)
趋同(经济学)
人工智能
数学
工程类
经济
大地测量学
经济增长
地理
生物化学
转录因子
化学
基因
抑制因子
人口学
航空航天工程
社会学
统计
作者
Vikas Palakonda,Jae-Mo Kang
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-13
标识
DOI:10.1109/tsmc.2023.3298690
摘要
Differential evolution (DE) has emerged as an effective technique for single-objective optimization problems (SOPs). Due to its efficient and straightforward framework, it has been extended further to address multiobjective optimization problems (MOPs). However, the existing multiobjective DE (MODE) algorithms focus on developing control strategies of mutation operators and parameters for a given population at every iteration, regardless of whether the population has insufficient distributions in objective space. Furthermore, several technical challenges exist when extending MODE approaches to deal with many-objective optimization problems (MaOPs). To break through such limitations, in this article, we propose a preference-inspired DE for multi and many-objective optimization (Pre-DEMO), which effectively and efficiently deals with a wide range of MOPs and MaOPs. First, a preference-inspired mutation operator is developed to generate individuals with good convergence and distribution properties. The local knee points are obtained among the nondominated individuals to articulate preferences in the mutation operator. Also, an adaptive strategy based on a clustering method is proposed to determine the local knee points. Second, a two-stage environmental selection is suggested in Pre-DEMO to preserve promising individuals for the next generations. Experimental results demonstrate that the Pre-DEMO approach outperforms the eight state-of-the-art algorithms on 35 benchmark problems.
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