A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D

Expensive multi-objective combinatorial optimization problems have constraints in the number of objective function evaluations due to time, financial, or resource restrictions. As most combinatorial problems, they are subject to a high number of duplicated solutions. Given the fact that expensive en...

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Veröffentlicht in:	Expert systems with applications Jg. 202; S. 117251
Hauptverfasser:	de Moraes, Matheus Bernardelli, Coelho, Guilherme Palermo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 15.09.2022 Elsevier BV
Schlagworte:	Algorithms Benchmarks Black-box optimization Combinatorial analysis Constraints Convergence Decomposition-based methods Diversity preservation Expensive multi-objective combinatorial optimization Multiple objective analysis Novel-First Tabu Search Oil fields Optimization Reproduction (copying) Search methods Tabu search Diversity preservation Expensive multi-objective combinatorial optimization Decomposition-based methods Novel-First Tabu Search Black-box optimization
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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