Evolutionary multi-objective optimization to attain practically desirable solutions

Kusuno, Natsuki; Aguirre, Hernán; Tanaka, Kiyoshi; Koishi, Masataka

doi:10.1145/2463372.2463457

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Many-Objective Evolutionary Algorithms

et al. 2015

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Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

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