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

Computers, Materials & Continua 2022年第5期71卷 2923-2941页

作者： erkan erdemir Adem Alpaslan Altun Department of Information Technologies Tokat Vocational and Technical Anatolian High SchoolMerkez/Tokat60030Turkey Department of Computer Engineering Faculty of TechnologyKonya Selcuk UniversitySelcuklu/Konya42130Turkey

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational *** 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 *** this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is *** 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 *** hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution *** works according to the best and worst food source *** this way,it is thought that the leader-follower salps will find the best solution to reach the food *** hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark *** 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 *** addition,these results were statistically compared using wilcoxon rank sum test with other *** 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.

关键词： Metaheuristic optimization benchmark algorithm swarm hybrid

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