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The Salp Swarm Algorithm (SSA) is one of the recently proposed nature-inspired metaheuristic algorithms. SSA mimics the life cycle of salp swarms. Salp swarm is an animal group which lived in oceans. The navigating and foraging behaviors are the characteristic properties of the salp swarms. These behaviors are modeled as an optimization algorithmin SSA and it is firstly proposed for solving continuous optimization problems. In literature, there is no binary version of this algorithm which uses transfer functions. In this work, SSA is modified for solving binary optimization problems by using transfer functions. Transfer functions are used to convert the continuous decision variables to the binary decision variables. With this modification, the structure of SSA has not been changed, but only the Sigmoid and the Tangent Hyperbolic transfer functions are adapted. In order to validate the performance of the proposed binary SSA, a well-known pure binary optimization problem, uncapacitated facility location problems (UFLP), set is considered. UFLPs are used for a benchmarking of many metaheuristic algorithms such as; artificial bee colony, tree-seed algorithm, particle swarm optimization, differential evolution and artificial algae algorithm. The experimental results of 12 UFLPs are compared with each other and state-of-art algorithms. Experimental results demonstrate that the SSA is a promising solver for lower dimensional problems, but its performance should be improved on higher dimensional problems.

International Conference on Advanced Technologies, Computer Engineering and Science
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Ersin Kaya Ahmet Cevahir Çınar Oğuzhan Uymaz Sedat Korkmaz Mustafa Servet Kıran