A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems

Zan Yang; Haobo Qiu; Liang Gao; Danyang Xu; Yuanhao Liu

doi:10.1016/j.ins.2022.11.021

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A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems

Zan Yang Haobo Qiu Liang Gao Danyang Xu Yuanhao Liu

Year: 2022 Journal: Information Sciences Vol: 619 Pages: 491-508 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2022.11.021

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Keywords:

Benchmark (surveying) Mathematical optimization Computer science Evolutionary algorithm Premature convergence Convergence (economics) Constraint (computer-aided design) Exploit Local search (optimization) Population Algorithm Mathematics Particle swarm optimization

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Advanced Multi-Objective Optimization Algorithms

Physical Sciences → Computer Science → Computational Theory and Mathematics

Metaheuristic Optimization Algorithms Research

Physical Sciences → Computer Science → Artificial Intelligence

Evolutionary Algorithms and Applications

Physical Sciences → Computer Science → Artificial Intelligence

A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems

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