Özet:

Metaheuristic algorithms provide approximate or optimal solutions for optimization problems in a reasonable time. With this feature, metaheuristic algorithms have become an impressive research area for solving difficult optimization problems. Snake Optimizer is a population-based metaheuristic algorithm inspired by the mating behavior of snakes. In this study, different chaotic maps were integrated into the parameters of the algorithm instead of random number sequences to improve the performance of Snake Optimizer, and Snake Optimizer variants using four different chaotic mappings were proposed. The performances of these proposed variants for eight different chaotic maps were examined on classical and CEC2019 test functions. The results revealed that the proposed algorithms contribute to the improvement of Snake Optimizer performance. In the comparison with the literature, the proposed Chaotic Snake Optimizer algorithm found the best mean values in many functions and took second place among the algorithms. As a result of the tests, Chaotic Snake Optimizer has been shown to be a promising, successful, and preferable algorithm.

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