Improved White Shark Algorithm Based on Chaotic Mapping and Quadratic Interpolation

To solve the problem of easily falling into local optima and low convergence accuracy of optimization results in white shark and optimization algorithms, an improved white shark optimization algorithm is proposed. In the population initialization phase, the sine chaotic mapping is used to initialize the population, enhancing the diversity of the population and improving the algorithm's search capability across the entire solution space; in the position update phase, a quadratic interpolation strategy is introduced to enhance the algorithm's local exploitation capability. Performance comparison experiments are conducted on 10 benchmark test functions, and the results show that the SWSO outperforms its comparative algorithms, with faster convergence speed and higher solution accuracy.