混合增强黑翅鸢优化算法及其应用OA

To address the limitations of the black-winged kite algorithm(BKA),which suffers from slow convergence and a tendency to get trapped in local optima,a hybrid enhanced black-winged kite algorithm(HEBKA)is proposed to signifi-cantly improve global search capability and optimization performance.HEBKA replaces the attack phase of BKA with the red-tailed hawk optimization algorithm and incorporates Bernoulli chaotic mapping as an attack adjustment factor.This modification streamlines the algorithmic process and substantially enhances global search efficiency,thereby accelerating convergence.Inspired by the pheromone mechanism of the black widow optimization algorithm,HEBKA divides the population into elite and inferior individuals.Elite individuals undergo migration operations to guide the population toward the global optimum,while inferior individuals are subjected to random perturbations to increase population diversity.This strategy reduces the blind reliance on leader migration,preventing premature convergence.When population clustering occurs,HEBKA applies an orthogonal experiment-based quasi-reflection perturbation strategy to the optimal individual.This approach leverages orthogonal experimental design to efficiently explore the solution space and introduces controlled perturbations via quasi-reflection to effectively escape local optima.To validate the effectiveness of these improvements,simulation experiments are conducted on the CEC2017 benchmark functions.Comparative analyses of convergence performance and Wilcoxon nonparametric statistical tests demonstrate that HEBKA significantly outperforms other algorithms in terms of convergence speed,optimization accuracy,and robustness,showcasing its superior global search capabilities and stability.HEBKA is applied to solving two-dimensional and three-dimensional traveling salesman problems(TSP),verifying its efficiency and practical potential in addressing complex real-world optimization challenges.