Self-adaptive Salp Swarm Algorithm for Optimization Problems

Abstract In this paper, an enhanced version of the Salp Swarm Algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) diversification of the SSA population referred as SSAstd, (ii) SSA parameters are tuned using a self-adaptive technique based Genetic Algorithm (GA) referred as SSAGA−tuner. The novelty of developing a self-adaptive SSA is to enhance its performance through balancing search exploration and exploitation. The enhanced SSA versions are evaluated using twelve benchmark functions. The diversified population of SSAstd enhances convergence behavior, and self-adaptive parameter tuning of SSAGA−tuner improves the convergence behavior as well, thus improving performance. The comparative evaluation against nine well-established methods shows the superiority of the proposed SSA versions. The enhancement amount in accuracy was between 2.97% and 99% among all versions of algorithm. In a nutshell, the proposed SSA version shows a powerful enhancement that can be applied to a wide range of optimization problems.