Abstract: The Salp Swarm Algorithm (SSA) is a population-based Meta-heuristic Algorithm (MA) that simulates the behavior of a group of salps foraging in the ocean. Although the basic SSA has stable exploration capability and convergence speed, it still can fall into local optimum when solving complex optimization problems, which may be due to low utilization of population information and unbalanced exploration-to-exploitation ratio. Therefore, this study proposes a Double Mutation Salp Swarm Algorithm (DMSSA). In this study, a Cuckoo Mutation Strategy (CMS) and an Adaptive DE Mutation Strategy (ADMS) are introduced into the structure of the original SSA. The former mutation strategy is summarized as three basic operations: judgment, shuffling, and mutation. The purpose is to fully consider the information among search agents and use the differences between different search agents to participate in the update of positions, making the optimization process both diverse in exploration and minor in randomness. The latter strategy employs three basic operations: selection, mutation, and adaptation. As the follower part, some individuals do not blindly adopt the original follow method. Instead, the global optimal position and differences are considered, and the variation factor is adjusted adaptively, allowing the new algorithm to balance exploration, exploitation, and convergence efficiency. To evaluate the performance of DMSSA, comparisons are made with numerous algorithms on 30 IEEE CEC2014 benchmark functions. The statistical results confirm the better performance and significant difference of DMSSA in solving benchmark function tests. Finally, the applicability and scalability of DMSSA to optimization problems with constraints are further confirmed in three experiments on classical engineering design optimization problems. The source code of the proposed algorithm will be available at: https://github.com/ncjsq/Double-Mutational-Salp-Swarm-Algorithm.

Key words: Salp swarm algorithm , · Meta-heuristic algorithm , · Global optimization , · Exploration , · Exploitation , · Bionic