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An overview on evolutionary algorithms for many‐objective optimization problems

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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
Main classification of many‐objective evolutionary algorithms according to the basic idea on which they are based
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