A novel two-stage constraints handling framework for real-world multi-constrained multi-objective optimization problem based on evolutionary algorithm

Multi-constrained multi-objective optimization is a challenging topic, which is very common in dealing with real-world problems. This paper proposes a novel two-stage ρ g / μ g framework based on multi-objective evolutionary algorithm (MOEA) to solve the multi-constrained multi-objective optimizatio...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 51; číslo 11; s. 8212 - 8229
Hlavní autori:	Li, Xin, An, Qing, Zhang, Jun, Xu, Fan, Tang, Ruoli, Dong, Zhengcheng, Zhang, Xiaodi, Lai, Jingang, Mao, Xiaobing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2021 Springer Nature B.V
Predmet:	Artificial Intelligence Computer Science Constraints Convergence Evolutionary algorithms Genetic algorithms Handling Machines Manufacturing Mechanical Engineering Multiple objective analysis Optimization Pareto optimization Processes Relaxation method (mathematics) Sorting algorithms Multi-constrained multi-objective optimization Two-stage constraints handling Dynamic stochastic ranking Constraint relaxation
ISSN:	0924-669X, 1573-7497
On-line prístup:	Získať plný text
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