QMOGWO: Multi-Objective Evolutionary Algorithm-Driven Path Optimization for Delivery Vehicles

The multi-objective evolutionary algorithm designed for optimizing delivery vehicle routes, particularly within the realm of rural agricultural logistics and transportation, adeptly addresses the myriad challenges encountered in rural distribution. In this regard, this paper presents the QMOGWO multi-objective evolutionary algorithm, which integrates the Q-Learning algorithm to tackle the vehicle path optimization issue. Through an analysis of nine standard test functions and empirical results derived from path planning in authentic rural settings, it is evident that the QMOGWO yields substantial economic benefits in resolving the distribution vehicle path optimization dilemma and is capable of producing a viable routing solution.