期刊论文

High-dimensional Multi-objective Optimization Strategy Based on Decision Space Oriented Search

中文

软件学报

1000-9825

2019

30

9

2686-2704

10.13328/j.cnki.jos.005842

61772178:国家自然科学基金 61502408:国家自然科学基金 61673331:国家自然科学基金 17A212:湖南省教育厅重点项目 2017JJ4001:湖南省自然科学基金 2016TP1020:湖南省科技计划

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传统的多目标进化算法（MOEA）对于低维连续的多目标优化问题已经具有良好的性能,但是随着优化问题目标维数的增加,优化难度也将剧增,主要原因是算法本身搜索能力不足,维数增加时选择压力变小,收敛性和分布性冲突难以平衡.利用连续多目标优化问题的特性,针对高维多目标优化的难点所在,提出了一种在决策空间的定向搜索策略（decision space,简称DS）,该策略可与基于支配关系的MOEA相结合.DS首先对优化问题进行采样分析,对问题特性进行解析,得到收敛性子空间控制向量和分布性子空间控制向量.将算法搜索过程分为收敛性搜索阶段和分布性搜索阶段,分别对应收敛性子空间和分布性子空间,在不同阶段搜索...

Traditional multi-objective evolutionary algorithm (MOEA) have sound performance when solving low dimensional continuous multi-objective optimization problems. However, as the optimization problems' dimensions increase, the difficulty of optimization will also increase dramatically. The main reasons are the lack of algorithms' search ability, and the smaller selection pressure when the dimension increases as well as the difficulty to balance convergence and distribution conflicts. In this study, after analyzing the characteristics of the continuous multi-objective optimization problem, a direc...