A Novel Hybrid Stochastic Searching Algorithm Based on ACO and PSO: A Case Study of LDR Optimal Design

摘要

With the rapid development of electronic commerce, the logistics distribution system brings to the widespread attention. And the logistics distribution routing (LDR) optimization is playing the very important role as one of core technologies in the logistics distribution system. This paper proposed a novel hybrid stochastic searching algorithm to solve the LDR optimization design problem, the algorithm unified the ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm effectively, which uses the randomness, the rapidity and the global characteristics of PSO to obtain the initial pheromone distribution firstly, then uses the ACO advantages of the concurrency, the positive feedback and the higher solving precision to find the exact solution. The results of simulation experiment show that the hybrid algorithm has superior global seeking optimization ability and the rapid convergence rate. The method is quick and effective to optimize the LDR problem, and can obtain the optimal solution or approximate optimal solution.