A self-adaptive dynamic multi-objective optimization algorithm based on transfer learning and elitism-based mutation

Dynamic multi-objective optimization problems (DMOPs) involve several conflicting objectives, and these objective functions change over time. Therefore, addressing DMOPs necessitates an effective response to environmental changes. However, most existing algorithms only deal with DMOPs with one parti...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 559; S. 126761
Hauptverfasser:	Zhang, Xi, Jin, Yaochu, Qian, Feng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 28.11.2023
Schlagworte:	Diversity introduction Dynamic multi-objective optimization Self-adaptive change response Transfer learning Self-adaptive change response Diversity introduction Dynamic multi-objective optimization Transfer learning
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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