Multi-swarm improved moth–flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems

作者：

Highlights：

• A multi-swarm improved moth–flame algorithm (MIMFO) is proposed.

• Chaotic grouping and dynamic regrouping are used to improve population diversity.

• Two sub-swarms are searched by spiral search and line search respectively.

• Gaussian mutation is used to mutate the optimal flame.

• 43 test problems and 57 engineering problems are used to evaluate the MIMFO.

摘要

•A multi-swarm improved moth–flame algorithm (MIMFO) is proposed.•Chaotic grouping and dynamic regrouping are used to improve population diversity.•Two sub-swarms are searched by spiral search and line search respectively.•Gaussian mutation is used to mutate the optimal flame.•43 test problems and 57 engineering problems are used to evaluate the MIMFO.

论文关键词：Moth–flame optimization algorithm,Multi-swarm,Gaussian mutation,Linear search,Engineering optimization problems

论文评审过程：Received 4 March 2022, Revised 27 April 2022, Accepted 8 May 2022, Available online 17 May 2022, Version of Record 26 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117562