期刊
出版社
WILEY PERIODICALS, INC
DOI: 10.1002/widm.1267
关键词
evolutionary algorithms; many-objective optimization; many-objective evolutionary algorithms
Multiobjective evolutionary algorithms (MOEAs) effectively solve several complex optimization problems with two or three objectives. However, when they are applied to many-objective optimization, that is, when more than three criteria are simultaneously considered, the performance of most MOEAs is severely affected. Several alternatives have been reported to reproduce the same performance level that MOEAs have achieved in problems with up to three objectives when considering problems with higher dimensions. This work briefly reviews the main search difficulties, visualization, evaluation of algorithms, and new procedures in many-objective optimization using evolutionary methods. Approaches for the development of evolutionary many-objective algorithms are classified into: (a) based on preference relations, (b) aggregation-based, (c) decomposition-based, (d) indicator-based, and (e) based on dimensionality reduction. The analysis of the reviewed works indicates the promising future of such methods, especially decomposition-based approaches; however, much still need to be done to develop more robust, faster, and predictable evolutionary many-objective algorithms. This article is categorized under: Technologies > Computational Intelligence
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据