多目标优化
进化算法
分类
数学优化
计算机科学
进化计算
比例(比率)
趋同(经济学)
人口
最优化问题
帕累托原理
采样(信号处理)
数学
算法
滤波器(信号处理)
物理
量子力学
社会学
人口学
经济增长
经济
计算机视觉
作者
Shufen Qin,Chaoli Sun,Yaochu Jin,Ying Tan,Jonathan E. Fieldsend
标识
DOI:10.1109/tevc.2021.3063606
摘要
It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.
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