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    An advanced Salp Swarm Algorithm for optimization problems
    (2023) Türkoğlu, Bahaeddin
    The Salp Swarm Algorithm (SSA) is a metaheuristic optimization algorithm inspired by Salp swarms' biological characteristics and colony strategies. There is a wide variety of studies conducted with SSA in the literature. These studies have revealed some significant disadvantages of SSA, the most critical being the imbalance of exploration and exploitation. In this study, an equilibrium operator has been developed using the Ikeda map. Thanks to this enhancement, the performance of the SSA algorithm has increased, and issues such as premature convergence and local optima have been overcome. To evaluate the proposed method, ten fixed-dimension benchmark problems and three engineering design optimization problems were solved. The proposed method is validated by comparing four well-known metaheuristic approaches and the original SSA. Experimental results demonstrated that the proposed method outperforms the compared methods.