ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 127; S. 65 - 77
Hauptverfasser:	Venske, Sandra M., Gonçalves, Richard A., Delgado, Myriam R.
Format:	Journal Article Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.03.2014 Elsevier
Schlagworte:	Adaptive algorithms Adaptive differential evolution Adaptive pursuit Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Credit assignment Decision theory. Utility theory Evolutionary algorithms Exact sciences and technology Matching Mathematical programming Multiobjective optimization Operational research and scientific management Operational research. Management science Optimization Probability matching State of the art Statistical tests Strategy Theoretical computing Credit assignment Probability matching Adaptive pursuit Multiobjective optimization Adaptive differential evolution Information credibility Probabilistic approach Pareto optimum Evolutionary algorithm Reinforcement learning Multiobjective programming Conceptual analysis Adaptive method Optimization Statistical test Semantics Greedy algorithm Differential evolution Version management Mathematical programming
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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