Research on Vehicle Routing Problem Based on Hybrid Optimization Algorithms

In this article, we conduct a comprehensive study of the vehicle routing problem based on hybrid optimization algorithms. We explore the implementation of this research using MATLAB development environment. First, we introduce the fundamental concepts of hybrid optimization algorithms and their applications in vehicle routing problems. We then discuss how to implement solution approaches for this problem using MATLAB's computational framework, including key functions like genetic algorithm (ga) and particle swarm optimization (pso) for global search combined with local search techniques. Our discussion covers various aspects of MATLAB programming, including algorithm parameter tuning, constraint handling, and solution visualization techniques, enabling readers to better understand the implementation methodology. Finally, we analyze and summarize our research findings to demonstrate the significance and contributions of hybrid optimization algorithm-based approaches to vehicle routing problem research. The implementation involves creating MATLAB functions for route encoding/decoding, fitness evaluation, and hybrid operator design to efficiently solve complex routing scenarios.