多策略改进的黑翅鸢优化算法及其工程实例应用OA

Aiming at the problems of unbalanced exploration and exploitation ability and easy to fall into local extremum in the black-winged kite optimization algorithm,multiple strategies improved black-winged kite optimization algorithm is proposed.In the algorithm attack stage,a phased dynamic hunting optimization mechanism is introduced to balance the global exploration and local exploitation capabilities of the algorithm.Random disturbance and nonlinear weight factors are introduced in the migration stage of the algorithm,which effectively avoids the risk of the algorithm falling into local optimum.The proposed algorithm is compared with various optimization algorithms on each test suite,and the experimental results are statistically analyzed by Wilcoxon rank sum test and Friedman test.The experimental results show that the pro-posed algorithm is significantly better than the comparison algorithms in terms of optimization accuracy,convergence per-formance and robustness,and the proposed algorithm is efficient and feasible in dealing with high-dimensional complex functions.In addition,the proposed algorithm is applied to solve the constrained optimization problems of 18 real-world engineering examples,which verifies the effectiveness and engineering practicability of the proposed algorithm.