Abstract
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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