Evolutionary salp swarm algorithm with multi-search strategies and advanced memory mechanism for solving global...

Real-world optimization problems, such as global optimization, cleaner production system, and complex design challenges are inherently complex, involving many variables and constraints. These factors make it challenging for optimizers to determine optimal solutions efficiently. Salp Swarm Algorithm (SSA) adapts easily to complex optimization problems due to its simplicity, multi-search strategy, and few control parameters. However, its search strategy lacks precision in guiding the population toward optimal regions of the solution space, which limits its effectiveness in optimizing cleaner production systems and complex design problems. This study proposes an evolutionary SSA (ESSA) to address complex optimization problems. ESSA proposes distinct innovative search strategies, including two evolutionary search strategies that enhance diversity and adaptive search, as well as an enhanced SSA search strategy that, while less exploratory, ensures steady convergence. ESSA introduces an advanced memory mechanism that stores the best and inferior solutions identified during optimization, enhancing diversity and preventing premature convergence. Moreover, it incorporates a stochastic universal selection method to regulate the archive by selecting individuals according to their fitness values. The performance of ESSA was evaluated using benchmark functions CEC 2017 and CEC 2020, compared to seven leading algorithms. Results show that ESSA outperforms SSA and others in solution quality and convergence speed. Statistical analyses confirm that ESSA ranks first and achieves the best optimization effectiveness, with values of 84.48%, 96.55%, and 89.66% for dimensions 30, 50, and 100, respectively, surpassing other optimizers. Additionally, ESSA's practical applicability is demonstrated through its success in optimizing a cleaner production system and solving complex design problems, highlighting its effectiveness in tackling challenging optimization tasks.