An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters
作者：
Highlights：
• An efficient version of the differential evolution (DE) named EFDE is proposed.
• Fitness-based dynamic mutation strategy and control parameters are developed.
• EFDE adopts parameter-free approach to adjust control parameters.
• Validation of EFDE is carried out on benchmark sets and engineering problems.
• Comparison of performance indicates the efficacy of the EFDE.
摘要
•An efficient version of the differential evolution (DE) named EFDE is proposed.•Fitness-based dynamic mutation strategy and control parameters are developed.•EFDE adopts parameter-free approach to adjust control parameters.•Validation of EFDE is carried out on benchmark sets and engineering problems.•Comparison of performance indicates the efficacy of the EFDE.
论文关键词：Global optimization,Differential evolution,Evolutionary state,Mutation strategy,Control parameters
论文评审过程：Received 6 February 2022, Revised 9 June 2022, Accepted 13 June 2022, Available online 18 June 2022, Version of Record 23 June 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109280
