Abstract:

Optimization problems occur in three different structures: continuous, discrete, and hybrid. Metaheuristic algorithms, which are frequently preferred in the solution of optimization problems today, are mostly proposed for continuous problems and are discretized with subsequent modifications. In this study, a novel binary version (Bin_MRFOA) of the manta ray foraging optimization algorithm, which was frequently used in the solution of continuous optimization problems before, was proposed to be used in the solution of binary optimization problems. The Bin_MRFOA was first tested on ten classical benchmark functions, and the effect of the transfer function on performance was examined by comparing the variants obtained using eight different transfer functions. Then the most successful Bin_MRFOA variant was run on the eighteen CEC2005 benchmark functions. The results were compared with the algorithms in the literature and interpreted with Wilcoxon signed-rank and Friedman tests, which are nonparametric tests. The results revealed that Bin_MRFOA is a successful, competitive, and preferable algorithm compared to the literature.

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