Quantum Wolf Pack Evolutionary Algorithm of Weight Decision‐Making Based on Fuzzy Control

In the traditional quantum wolf pack algorithm, distribution is simplified, and leader randomly selected. This leads to problems that development exploration ability of algorithm weak rate convergence slow. Therefore, a evolutionary weight decision-making based on fuzzy control proposed in this paper. First, realize diversification regular selection wolf, dual strategy method sliding mode cross principle are adopted optimize initial position candidate wolf. Second, new non-linear factor improve wolf's search direction operator enhance local capability algorithm. Meanwhile, weighted evolution computation used update optimization Then, functional analysis prove thus realizing feasibility algorithm's global convergence. The performance was verified through six standard test functions. results compared with Results show improved had faster convergence, higher precision, stronger ability.