[2023-Vol.20-Issue 1]Double Mutational Salp Swarm Algorithm: From Optimal Performance Design to Analysis
Post: 2023-02-05 11:50  View:261

Journal of Bionic Engineering (2023) 20:184–211  

Double Mutational Salp Swarm Algorithm: From Optimal Performance Design to Analysis 

Chao Lin1  · Pengjun Wang2  · Xuehua Zhao3  · Huiling Chen1 

1 College of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, China
2 College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China
3 School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China

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, shufing, and mutation. The purpose is to fully consider the information among search agents and use the diferences between diferent 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 diferences are considered, and the variation factor is adjusted adaptively, allowing the new algorithm to balance exploration, exploitation, and convergence efciency. To evaluate the performance of DMSSA, comparisons are made with numerous algorithms on 30 IEEE CEC2014 benchmark functions. The statistical results confrm the better performance and signifcant diference of DMSSA in solving benchmark function tests. Finally, the applicability and scalability of DMSSA to optimization problems with constraints are further confrmed in three experiments on classical engineering design optimization problems. The source code of the proposed algorithm will be available at: Double-Mutational-Salp-Swarm-Algorithm. 

Keywords  Salp swarm algorithm · Meta-heuristic algorithm · Global optimization · Exploration · Exploitation · Bionic

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