| Summary: | Abstract Yellow Saddle Goatfish Algorithm (YSGA) is an optimization model inspired by the hunting behavior of yellow saddle goatfish which emulates their collaborative behaviors with chaser fish and blocker fish. To improve the global convergence, chaotic maps have been combined with YSGA in this paper. Chaotic is a nonlinear deterministic system that displays complex, noisy‐like, and unpredictable behavior. Due to its non‐repetitive nature, an overall search can be carried out at a higher speed. The proposed algorithm is based on the excellence of the chaotic searching using a multi‐chaotic approach and the YSGA optimization, which has been applied to 68 benchmark functions. The results of the proposed Multi‐Chaotic Yellow Saddle Goatfish algorithm are compared with YSGA and also with nine other states of art meta‐heuristic algorithms. The results show that the proposed algorithm improves the performance of the YSGA algorithm.
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