Circle Chaotic Search-Based Butterfly Optimization Algorithm

Aiming at a series of problems of traditional Butterfly Optimization Algorithm (BOA), such as lower solution accuracy, slower convergence speed, sensitivity to local optimum, and poorer stability, an improved butterfly optimization algorithm based on circle chaotic mapping and chaotic search strategy (CSBOA) is proposed. In CSBOA, the circle chaotic mapping is used for population initialization to obtain better initial solutions. Meanwhile, the chaotic search strategy is used to quadratically find the best value after global optimization to improve the algorithm's ability to jump out of local optima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.