A Novel Artificial Jellyfish Search Algorithm Improved with Detailed Local Search Strategy

Abstract

Metaheuristic algorithms are frequently preferred algorithms for optimization problems in many different fields. The number of these algorithms inspired by natural phenomena is increasing day by day. Artificial Jellyfish Search Algorithm, inspired by the behavior of jellyfish in the ocean, is one of the new metaheuristic algorithms that have been proposed recently. In this study, two different search strategies were used together with a modification made in the active motion of the Artificial Jellyfish Search Algorithm, in the local search section, and the local search capability of the algorithm was thus developed. With this modification, it is aimed to preserve population diversity for a longer time. The proposed algorithm has been tested for 10, 30, and 50 dimensions on single-objective CEC2017 benchmark functions. The results obtained were compared with the standard algorithm and algorithms selected from the literature and interpreted with the help of statistical tests. It has been determined that the proposed algorithm outperforms the standard algorithm and becomes competitive with other algorithms in the literature thanks to the modification made. © 2021 IEEE