Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U.S. Postal Service system.

Automated summary

This paper explores the use of elitist genetic algorithm for electric vehicle routing problem with time window, introducing an improved neighbor routing initialization method. It enhances convergence speed by adjusting adaptive crossover and mutation probabilities. Experimental studies demonstrate the algorithm's effectiveness in both random and benchmark cases.