A surrogate-assisted multi-objective particle swarm optimization of expensive constrained combinatorial optimization problems

作者：

Highlights：

• A surrogate-assisted multi-objective particle swarm optimization algorithm is proposed.

• Using adaptive stochastic ranking strategy to improve the quality of parent individuals.

• A novel state updating mechanism is developed to guide the searching of particles.

摘要

•A surrogate-assisted multi-objective particle swarm optimization algorithm is proposed.•Using adaptive stochastic ranking strategy to improve the quality of parent individuals.•A novel state updating mechanism is developed to guide the searching of particles.

论文关键词：Data-driven optimization,Constrained combinatorial optimization,Expensive problems,Multi-objective particle swarm optimization (MOPSO),Surrogate model,Random forest

论文评审过程：Received 3 October 2020, Revised 7 March 2021, Accepted 14 April 2021, Available online 21 April 2021, Version of Record 23 April 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107049