A Novel Artificial Jellyfish Search Algorithm Improved with a Differential Evolution Algorithm-Based Global Search Strategy

Abstract

Metaheuristic algorithms are algorithms inspired by natural phenomena and that are used to decide which possible solution is more efficient to solve a problem. Although these algorithms, whose numbers are increasing day by day, do not guarantee the exact solution, they promise to reach a solution around the exact solution quickly. Artificial Jellyfish Search Algorithm (YDA) is also a new metaheuristic algorithm proposed in 2021. In this study, a modification has been made to the global search part of the standard algorithm in order to improve the global search capability of YDA. Accordingly, the "current-to-best" approach, which is one of the successful mutation strategies in the Differential Evolution Algorithm, has been integrated into the global search method of YDA. The advanced algorithm (MYDA) obtained as a result of this modification has been tested for 10,30,50,100,500 and 1000 dimensions on a total of twelve benchmark functions, seven of which are uni-modal and five are multi-modal. In addition, MYDA has also been compared with algorithms selected from the literature. The results have been interpreted with the help of statistical tests. When the results obtained are examined, it has been determined that the proposed algorithm outperforms the standard algorithm for all dimensions in all functions. In the comparison with the literature, it has been determined that the algorithm produces successful and competitive results.