【摘要】 针对物流配送中心选址模型具有多约束和非线性的特点,导致难以求解的问题。提出一种改进灰狼优化算法的求解策略。文章通过引入交叉变异策略,改进了传统灰狼算法在迭代后期易早熟收敛的问题;通过加入双种群寻优策略,丰富了灰狼算法的种群多样性,提高了算法的收敛速度。将改进后的灰狼算法针对物流配送中心选址模型进行求解,实验结果表明,该改进灰狼优化算法具有较高的全局搜索能力,针对物流配送中心选址模型具有较高的搜索精度,很大程度的提高了物流配送效率。更多还原

【Abstract】 Aiming at the problem that the location model of logistics distribution center has the characteristics of multi constraints and nonlinearity, which is difficult to be solved, an improved gray wolf optimizer(GWO) is proposed. In this paper, by introducing the cross mutation strategy, the issue of premature convergence of the traditional GWO in the later stage of iteration is improved;by adding the dual population optimization strategy, the population diversity of GWO is enriched and the convergence speed of the algorithm is increased. Using the improved GWO to solve the location model of logistics distribution center, the experimental results show that the improved GWO has high global search ability, and high search accuracy for the logistics distribution center location model, which greatly improves the logistics distribution efficiency.更多还原