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A New Metaheuristic Approach to Solving Benchmark Problems: Hybrid Salp Swarm Jaya Algorithm

Erkan Erdemir1,*, Adem Alpaslan Altun2
1 Department of Information Technologies, Tokat Vocational and Technical Anatolian High School, Merkez/Tokat, 60030, Turkey
2 Department of Computer Engineering, Faculty of Technology, Konya Selcuk University, Selcuklu/Konya, 42130, Turkey
* Corresponding Author: Erkan Erdemir. Email: ,
(This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 71(2), 2923-2941. https://doi.org/10.32604/cmc.2022.022797

Received 19 August 2021; Accepted 05 October 2021; Issue published 07 December 2021

Abstract

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost. As with other types of algorithms, in metaheuristic algorithms, one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms. In this study, a hybrid algorithm (HSSJAYA) consisting of salp swarm algorithm (SSA) and jaya algorithm (JAYA) is designed. The speed of achieving the global optimum of SSA, its simplicity, easy hybridization and JAYA's success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms. The hybrid algorithm is based on SSA's leader and follower salp system and JAYA's best and worst solution part. HSSJAYA works according to the best and worst food source positions. In this way, it is thought that the leader-follower salps will find the best solution to reach the food source. The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions. The results were compared with SSA, JAYA, cuckoo search algorithm (CS), firefly algorithm (FFA) and genetic algorithm (GA). As a result, a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained. In addition, these results were statistically compared using wilcoxon rank sum test with other algorithms. According to the statistical results obtained from the results of the benchmark functions, it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

Keywords

Metaheuristic; optimization; benchmark; algorithm; swarm; hybrid

Cite This Article

E. Erdemir and A. Alpaslan Altun, "A new metaheuristic approach to solving benchmark problems: hybrid salp swarm jaya algorithm," Computers, Materials & Continua, vol. 71, no.2, pp. 2923–2941, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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