Refining differential evolution with mutation rate and neighborhood weight local search

• Published in: Cluster computing Vol. 27; no. 4; pp. 4361 - 4384
• Main Authors: Sun, Lisheng, Ma, Yongjie, Pan, Yuhua, Wang, Minghao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Competition; Computer Communication Networks; Computer Science; Evolutionary algorithms; Evolutionary computation; Exploitation; Genetic algorithms; Heuristic methods; Mutation; Neighborhoods; Operating Systems; Optimization; Parameter sensitivity; Processor Architectures; Search algorithms; Solution space; Mutation rate; Parameter adaptation; Neighborhood weight local search; Multi strategy
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-023-04173-w

Differential Evolution (DE) is a population-based metaheuristic search algorithm that exhibits excellent performance. However, it is sensitive to mutation strategies and control parameters.To mitigate the impact of these factors, this paper proposes refining differential evolution with mutation rate and neighborhood weight local search(MRNLDE). The algorithm guides individuals in selecting an appropriate mutation mode through the mutation rate, effectively utilizing the evolutionary information inherent in the individuals themselves, thereby enhancing the search efficiency of the algorithm. Furthermore, the neighborhood individual information is utilized to re-explore the solution space, allowing for a comprehensive exploration of individual evolutionary potential and enhancing the algorithm’s diversity. The performance of MRNLDE was validated under two sets of benchmark problems at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Computing in Evolution (CEC), and the results show that MRNLDE performs well overall.