进化算法
水准点(测量)
数学优化
变量(数学)
趋同(经济学)
公制(单位)
计算机科学
人口
多目标优化
进化计算
遗传算法
最优化问题
数学
机器学习
工程类
地理
数学分析
经济
人口学
社会学
经济增长
运营管理
大地测量学
作者
Ying Xu,Chong Xu,Huan Zhang,Lei Huang,Yiping Liu,Yusuke Nojima,Xiangxiang Zeng
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:53 (11): 6998-7007
被引量:17
标识
DOI:10.1109/tcyb.2022.3180214
摘要
Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on each objective, so decision variables can be divided into convergence- or diversity-related variables. In this article, we propose a new metric called the optimization degree of the convergence-related decision variable to each objective to calculate the contribution objective of each decision variable. All decision variables are grouped according to their contribution objectives. Then, a multiobjective evolutionary algorithm, namely, decision variable contributing to objectives evolutionary algorithm (DVCOEA), has been proposed. In order to balance the convergence and diversity of the population, the DVCOEA algorithm combines the multipopulation multiobjective framework, where two different optimization strategies are designed to optimize the subpopulation and individuals in the external archive, respectively. Finally, DVCOEA is compared with several state-of-the-art algorithms on a number of benchmark functions. Experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI