Logistics Path Planning based on Improved Particle Swarm Optimization Algorithm

In order to solve the problem of vehicle path scheduling management more reasonably, the author proposes a logistics path planning research based on improved particle swarm optimization algorithm. The author introduces a particle swarm optimization algorithm that incorporates a dynamic monkey jumping mechanism. Initially, dynamic population grouping is used to assign varying dynamic inertia weights, enhancing the algorithm’s speed. Subsequently, the monkey jumping mechanism is added to ensure global convergence. This enhanced algorithm was then tested on two logistics distribution path optimization scenarios. In a consistent environment, the improved algorithm outperformed the standard particle swarm optimization algorithm by achieving a better optimal path fitness value, shorter average operation time, and a higher number of successful attempts to find the optimal solution. The experimental results show that out of 10 instances solved using the improved algorithm, 5 times obtained the optimal solution of 67.1km, and the optimal delivery path corresponding to the optimal solution was 0-4-7-6-0; 0-2-8-5-3-1-0, with an average calculation time of 1.26s, indicating high computational efficiency. The total delivery distance and average calculation time of the particle swarm algorithm, as well as the number of times to obtain the optimal solution, are 69.01, 2.7, and 3, respectively. It is evident that the enhanced particle swarm optimization algorithm significantly outperforms the conventional particle swarm algorithm. The improvements not only accelerate the optimization process but also enhance the algorithm’s convergence, ensuring high-quality optimization results. Consequently, this improved algorithm holds substantial application value.