Paper Info
Title
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 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% 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.
Year
DOI
Venue
2022
10.1007/s00500-022-07280-9
Soft Computing
Keywords
DocType
Volume
Salp swarm algorithm, Initial population diversity, Self-adaptive parameters tuning, Swarm algorithms, Optimization, Metaheuristic
Journal
26
Issue
ISSN
Citations 
18
1432-7643
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Sofian Kassaymeh101.35
Salwani Abdullah200.68
Mohammed Azmi Al-Betar301.01
Mohammed Alweshah4112.53
Mohamad Al-Laham500.34
Zalinda Othman600.34