Abstract: Aiming at the drawbacks of beluga whale optimization (BWO), such as low convergence accuracy, limited adaptive ability and weak anti-stagnation ability, two improved BWO algorithms based on Fuch mapping and dynamic opposition-based learning, namely, CIOEBWO and CPOEBWO, are proposed from the perspectives of chaos initialization, chaotic parameter, and nonlinear control strategy. Fuch chaotic initialization is used to increase the traversal of the initial population of BWO, which enhances the optimization accuracy and convergence speed of the algorithm. In the phase of exploitation, Fuch chaotic mapping is introduced to dynamically adjust the parameter C1 to coordinate the capabilities of global search and local search, which improves the adaptive ability of BWO effectively. On the basis of two improvement strategies described above, the dynamic opposition-based learning strategy is introduced to enrich the number of high-quality individuals and enhance the overall anti-stagnation ability of the algorithm. The experimental results of 8 benchmark test functions and Friedman rank test indicate that the convergence accuracy, adaptive ability, and anti-stagnation ability of improved BWO are effectively improved. Compared with BWO and CIOEBWO, CPOEBWO has the better performance. In addition, the optimization results of CPOEBWO and six comparison algorithms show that CPOEBWO has the stronger optimization ability and robustness. Finally, CPOEBWO is applied to solve the engineering optimization problems to demonstrate its applicability and effectiveness.

Key words: beluga whale optimization algorithm, Fuch mapping, dynamic opposition-based learning, chaotic parameter, engineering optimization problems