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

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.

Website Phishing Technique Classification Detection with HSSJAYA Based MLP Training

“…Developed by Erdemir & Altun [55], HSSJAYA covers the leader and follower position update sections of SSA, it was created by taking into account the best and worst approach of JAYA. In this algorithm, the targeted position for the salps to get closer to the food is expressed as the best food source.…”

“…MLP trained with HSSJAYA and other algorithms were analyzed for website phishing classification detection. Accordingly, the HSSJAYA algorithm was successful in optimizing benchmark functions compared to the algorithms it was compared to [55] and was also successful in training MLP compared to the algorithms it was compared to. In other words, the MLP trained with HSSJAYA was determined according to the experimental results, which it detected better than other algorithms compared to determine whether websites are phishing, suspicious or legal, according to the attributes in the website phishing dataset used in the study.…”

“…In this study, a new MLP training technique has been proposed for the detection of website phishing technique. MLP training was carried out using the Hybrid Salp Swarm Jaya algorithm (HSSJAYA) developed by Erdemir & Altun [55]. The reason for choosing this algorithm in MLP training is that it is a new hybrid algorithm, and its success in solving benchmark problems.…”