A dual-grid dual-phase strategy for constrained multi-objective optimization

Constrained multi-objective optimization problems (CMOPs) appear frequently in engineering applications. In some cases, feasible regions are narrow and/or disconnected. For this kind of problems, existing constraint-handling methods, integrated with multi-objective evolutionary algorithms, are easil...

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Veröffentlicht in:	2019 IEEE Symposium Series on Computational Intelligence (SSCI) S. 1881 - 1888
Hauptverfasser:	Ming, Mengjun, Wang, Rui, Zhang, Tao, Ishibuchi, Hisao
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2019
Schlagworte:	constrained multi-objective optimization problem (CMOP) Convergence dual-grid push and pull evolutionary algorithms Evolutionary computation MOEA/D-DPPS Pareto optimization Sociology Switches
Online-Zugang:	Volltext
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