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.
Highlights
Optimization problems; signal processing, mathematics, chemistry, computer science, mechanics, economics, etc. it is the expression of real-world problems in fields by converting them into mathematical terms
The aim of this study is to develop a new hybrid algorithm by combining the metaheuristic optimization algorithms that exist in the literature
The results obtained were compared with the popular cuckoo search algorithm (CS), genetic algorithm (GA), firefly algorithm (FFA) algorithms, primarily the salp swarm algorithm (SSA) and jaya algorithm (JAYA) algorithms that make up the hybrid algorithm
Summary
Optimization problems; signal processing, mathematics, chemistry, computer science, mechanics, economics, etc. it is the expression of real-world problems in fields by converting them into mathematical terms. Optimization problems; signal processing, mathematics, chemistry, computer science, mechanics, economics, etc. It is the expression of real-world problems in fields by converting them into mathematical terms. Purpose in optimization problems; is to find the best available solution by optimizing the value among the possible solutions within a certain solution search range and constraints [1,2]. It may be necessary to use different algorithms to find the best solution to optimization problems. These algorithms are divided into deterministic algorithms, which often use a method of tracking a particular sequence of actions, and stochastic algorithms that contain randomness [3]. As long as the input given to the problem in the algorithm is the same, the solution obtained as the output is always the same, but with a deterministic algorithm, structural difficulties can develop in solving problems, and there is a possibility that the expected solution cannot be obtained [4,5]
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