Analysis on Population Size and Neighborhood Recombination on Many-Objective Optimization

Kowatari, Naoya; Oyama, Akira; Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1007/978-3-642-32964-7_3

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…In addition to the previously mentioned approaches, there have been some other topics closely related to MaOPs, such as a large population size [Kowatari et al 2012;Aguirre et al 2013a], customized mutation and crossover operators [Sato et al 2011a], nondominated sorting methods , and better crowding distance measures [Wang et al 2010;Minku and Yao 2013].…”

Section: Discussionmentioning

confidence: 99%

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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