A multi-objective hyper-heuristic based on choice function

•A learning selection hyper-heuristic is proposed for multi-objective optimization.•A choice function utilized within the framework for multi-objective optimization.•Three MOEAs (NSGAII, SPEA2, and MOGA) are mixes and exploited their strengths.•The proposed method performs better than three MOEAs an...

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Veröffentlicht in:	Expert systems with applications Jg. 41; H. 9; S. 4475 - 4493
Hauptverfasser:	Maashi, Mashael, Özcan, Ender, Kendall, Graham
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.07.2014 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Amalgams Applied sciences Artificial intelligence Computer science; control theory; systems Decision theory. Utility theory Evolutionary algorithm Exact sciences and technology Heuristic Hyper-heuristic Learning Learning and adaptive systems Low level Mathematical analysis Mathematical models Metaheuristic Multi-objective optimization Operational research and scientific management Operational research. Management science Optimization Searching Theoretical computing Metaheuristic Multi-objective optimization Evolutionary algorithm Hyper-heuristic Legged locomotion Multiobjective programming Selection function Adaptive method Optimization Search algorithm Walking Vertebrata Experimental result Pisces Heuristic method Artificial intelligence Metamodel Mathematical programming
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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