An Indicator-Based Many-Objective Evolutionary Algorithm With Boundary Protection

Many-objective optimization problems (MaOPs) pose a big challenge to the traditional Pareto-based multiobjective evolutionary algorithms (MOEAs). As the number of objectives increases, the number of mutually nondominated solutions explodes and MOEAs become invalid due to the loss of Pareto-based sel...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 51; H. 9; S. 4553 - 4566
Hauptverfasser:	Liang, Zhengping, Luo, Tingting, Hu, Kaifeng, Ma, Xiaoliang, Zhu, Zexuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">I ϵ⁺ indicator boundary protection Computational complexity Convergence coverage diversity Diversity reception evolutionary algorithm Evolutionary algorithms Evolutionary computation Genetic algorithms many-objective evolutionary algorithm (MaOEA) multiobjective evolutionary algorithm (MOEA) Multiple objective analysis Optimization Sociology Statistics Strategy
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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