A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper proposes a grid-based...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 17; H. 5; S. 721 - 736
Hauptverfasser:	Yang, Shengxiang, Li, Miqing, Liu, Xiaohui, Zheng, Jinhua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2013 Institute of Electrical and Electronics Engineers
Schlagworte:	Applied sciences Artificial intelligence Computer science; control theory; systems Computer systems and distributed systems. User interface Convergence diversity Evolutionary computation evolutionary multiobjective optimization (EMO) Exact sciences and technology grid Learning and adaptive systems many-objective optimization Measurement Optimization Sociology Software Statistics Vectors High performance Archive Dominating set Scalability Pareto optimum Hierarchical classification Evolutionary algorithm Competitiveness Grid Multiobjective programming Dominance many-objective optimization Distributed computing Optimization Convergence Uniform distribution Experimental result diversity evolutionary multiobjective optimization (EMO) Mathematical programming
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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