Self-adaptive salp swarm algorithm for optimization problems

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 SSA population referred as $$_{std}$$ , (ii) parameters are tuned using a self-adaptive technique-based genetic (GA) $$_{GA-tuner}$$ . The novelty developing is to enhance its performance through balancing search exploration and exploitation. versions evaluated twelve benchmark functions. diversified enhances convergence behavior, parameter tuning improves behavior well, thus improving performance. comparative evaluation against nine well-established methods shows superiority proposed versions. enhancement amount in accuracy between 2.97 99% among all algorithm. nutshell, powerful that can be applied wide range problems.