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

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