A novel metaheuristic algorithm: advanced social memory optimization

• DOI: 10.1088/1402-4896/adbfd6
• Publication Date: 2025-03-12T22:53:17Z
• Authors: Shijie Fan; Ruichen Wang; Kang Su
• Keywords: Optimization algorithm; Parallel metaheuristic

Abstract The field of optimization problems has garnered significant attention due to its importance across various appli-cations, particularly driven by the demand for efficient solutions complex engineering challenges. Numerous metaheuristic algorithms inspired animal behaviour or swarm intelligence have been proposed; however, these often pursue a multitude strategies, resulting in excessive parameters that complicate tuning and hinder convergence balance. Additionally, based on human remain scarce. To address limitations, extensive research conducted interplay between social memory individual memory, leading introduction novel human-behaviour-inspired algorithm, named Ad-vanced Social Memory Optimization (ASMO). This algorithm seeks complexities parameter management, convergence, balance more effectively with streamlined set strategies. Furthermore, mathematical model mechanisms formation updating un-derpins algorithm. Rigorous performance evaluations, utilizing Wilcoxon Rank-Sum Test Fried-man multiple benchmark suites (CEC2017, CEC2019, CEC2022), demonstrate ASMO, only two algorithmic outperforms matches established than half test functions. These findings suggest promising new avenues and, given succinctness ASMO’s underscore potential as powerful tool enhancing developing so-lutions design problems. code ASMO is available Appendix D.