Abstract: The main idea of the logistics distribution center location problem is to maximize efficiency and minimize costs. In order to quickly obtain a reasonable logistics distribution center location scheme, this paper proposes a quasi-opposition learning spider monkey optimization algorithm based on Laplace distribution（LOBSMO） to solve this problem. Firstly, the model of logistics distribution center location is established. Then, in the basic spider monkey optimization algorithm, the Laplace distribution is used to initialize the swarm of spider monkeys. It uses the exponential decreasing and stochastic logarithmic decreasing strategy to improve the step factor in the local leader phase. In the global leader phase, a new search mechanism and a quasi-opposite learning strategy in the local leader decision phase are adopted to improve the performance of the algorithm. Finally, simulation results show that the proposed method is feasible.

Key words: logistics distribution center, spider monkey optimization algorithm, Laplace distribution, quasi-opposite learning, nonlinear strategy

摘要： 物流配送中心选址问题的核心是效率最大化，成本最小化。为了快速得到合理的物流配送中心选址方案，现提出一种基于Laplace分布的伪反向蜘蛛猴优化算法（LOBSMO）来求解此问题。建立物流配送中心选址模型。在基本蜘蛛猴优化算法中，采用了Laplace分布初始化蜘蛛猴种群，在局部领导阶段用指数递减与随机对数递减策略改进步长因子，在全局领导阶段提出了新的搜索机制及局部领导决策阶段的伪反向学习策略来提高算法的寻优性能。最后，通过仿真实验说明该方法是可行的。

关键词: 物流配送中心, 蜘蛛猴优化算法, Laplace分布, 伪反向学习, 非线性策略