Abstract
The Crab Algorithm is an evolutionary algorithm inspired by the behavior of crabs, which balances global search and local optimization by simulating the behavioral strategies of crabs. This paper introduces chaos theory and proposes a chaos disturbance strategy. Chaos variables are introduced into the iteration process of the Crab Algorithm to perturb the position and velocity of individuals, thereby escaping local optima and achieving global optimization. Several test functions are used to compare the new algorithm with the original one, yielding better results. In the application of parameter identification of Fresnel refractive index, an optimization model is constructed, and the improved Crab Algorithm is applied to solve the model. The experimental results show that compared with the traditional Crab Algorithm, the improved algorithm proposed in this paper has significantly improved identification accuracy and convergence speed. In addition, by comparing with other optimization algorithms, the superiority of this paper's algorithm is verified.
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