Optimizasyon Problemleri için Gelişmiş Salp Sürüsü Algoritması

Abstract

Salp Swarm Algorithm (SSA) is metaheuristic optimization algorithm inspired by the biological characteristics and colony strategies of salp swarms. There are a wide variety of studies conducted with SSA in the literature. In these studies, it was also emphasized that SSA has very critical main disadvantages. The most important of these disadvantages is the imbalance of exploration and exploitation. In this study, an equilibrium operator is developed using the ikeda chaotic map. Thanks to this improvement, the performance of the SSA algorithm has been increased and early convergence and stuck to local optima problems has been overcome. To evaluate the success of the proposed method, ten different fixed dimension benchmark problems and three popular engineering design optimization problems are solved. The reliability of the proposed method has been verified by comparing it with four well-known metaheuristic approaches and the original SSA. Experimental study results confirmed that the proposed method outperforms the compared methods.