【摘要】 针对基本布谷鸟算法求解物流配送中心选址问题时存在搜索精度低、易陷入局部最优值的缺陷,提出一种改进的布谷鸟算法.算法采用基于寄生巢适应度值排序的自适应方法改进基本布谷鸟算法的惯性权重,以平衡算法的全局开发能力和局部探索能力;利用NEH领域搜索以提高算法的搜索精度和收敛速度;引入停止阻止策略对全局最优寄生巢位置进行变异避免算法陷入局部最优值、增加种群的多样性.通过实验仿真表明,改进的布谷鸟算法在求解物流配送中心选址问题上要优与基本布谷鸟算法以及其它智群算法,是一种有效的算法.更多还原

【Abstract】 An improved cuckoo algorithm is presented for solving the problem of logistics distribution center location,which overcomes the disadvantages of the basic cuckoo algorithm,such as low search accuracy and ease of falling into local optimal value.In the proposed algorithm,to balance the global development and local search capabilities,the inertia weight is modified using an adaptive method based on the fitness value sort of parasitic nests.The NEH domain method is adopted to enhance the searching precision and the rate of convergence.In addition,to increase the diversity of the population and avoid the local optimum,a stop strategy is introduced to mutate the position of the global optimal parasitic nests.The simulation shows that the improved cuckoo algorithm has better performances compared with the basic cuckoo algorithm and other wisdom group algorithms.更多还原