Self-adaptive salp swarm algorithm for optimization problems
Soft Computing(2022)
摘要
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 SSA _std , (ii) SSA parameters are tuned using a self-adaptive technique-based genetic algorithm (GA) referred as SSA _GA-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 SSA _std enhances convergence behavior, and self-adaptive parameter tuning of SSA _GA-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
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关键词
Salp swarm algorithm, Initial population diversity, Self-adaptive parameters tuning, Swarm algorithms, Optimization, Metaheuristic
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