DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization
作者：
Highlights：
• Proposing an effective multi-trial vector approach (EMTV) armed by diversity maintaining.
• Developing a diversity-maintained differential evolution algorithm (DMDE) using EMTV.
• Introducing an archiving mechanism to keep solutions and enhance population diversity.
• Introducing a mechanism to track individuals' behavior and enrich population diversity.
• DMDE is competitive to solve non-decomposition large-scale and real-world problems.
摘要
•Proposing an effective multi-trial vector approach (EMTV) armed by diversity maintaining.•Developing a diversity-maintained differential evolution algorithm (DMDE) using EMTV.•Introducing an archiving mechanism to keep solutions and enhance population diversity.•Introducing a mechanism to track individuals' behavior and enrich population diversity.•DMDE is competitive to solve non-decomposition large-scale and real-world problems.
论文关键词：Optimization,Metaheuristic,Differential evolution algorithm,Multi-trial vector (MTV) approach,Large-scale global optimization,Non-decomposition large-scale global optimization
论文评审过程：Received 5 May 2021, Revised 16 September 2021, Accepted 11 March 2022, Available online 17 March 2022, Version of Record 25 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116895
