Path Optimization Based on Ant Colony Algorithm and Simulated Annealing Algorithm

Abstract

In this paper, a collaborative distribution model based on intelligent optimization algorithms is proposed, focusing on how to optimize the distribution paths and costs through K-means algorithm, genetic algorithm, ant colony algorithm and simulated annealing algorithm. First, the K-means algorithm is utilized to cluster the demand points and delineate the regions to provide support for the subsequent path planning. Second, in optimizing the delivery path, genetic algorithm and ant colony algorithm are used to solve the TSP and the optimal flight path of the UAV, respectively, which in turn calculates the shortest path and the minimum delivery cost. Finally, in order to reduce the total cost, a simulated annealing algorithm is used to solve the number of drones and the scheduling scheme to ensure that the fixed cost is minimized, and the type of drones is rationally selected according to different task requirements. The model realizes the improvement of distribution efficiency and cost reduction through the cooperative work of effective multiple delivery tools. The model achieves path optimization and reasonable scheduling of resources in complex distribution tasks through the combined application of intelligent algorithms, and at the same time ensures the optimal use of different types of delivery tools in the distribution process, which makes the model not only have strong practical applicability, but also high computational efficiency and scalability.