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

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