Adaptive Elimination Particle Swarm Optimization Algorithm for Logistics Scheduling

The particle swarm optimization algorithm sets few parameters, is easy to operate, and has now been successfully applied to various optimization problems. For the logistics scheduling problem, in the face of complex, large-scale search solution space will consume many resources. The standard particle swarm optimization can falling into local optimum easily when facing more complex problems. In this paper, three optimization strategies are designed to address the above problems, and the adaptive elimination particle swarm optimization (AELPSO) algorithm is proposed to optimize the logistics scheduling problem with a path planning scheme based on the principle of greedy strategy. The inertia weight linear decreasing method and asynchronous change adjusting learning factor method are introduced to reasonably balance the exploration and exploitation ability of the particle swarm. Combined with the idea of natural selection, the search efficiency of particles is enhanced. In the experimental part of this paper, AELPSO is used to solve the logistics scheduling problem on eight data sets and compared and analyzed. The experimental results show that the AELPSO algorithm can significantly improve the quality of the solution, and at the same time, it can obtain a better path planning scheme for the logistics scheduling problem.