Solving electric vehicle routing problem using genetic algorithm and ant colony optimization

Abstract

The increasing number of electric vehicles (EVs) has led to the need for new methods for routing EVs. The traditional vehicle routing problem (VRP) is not well-suited for EVs, as it does not consider the limited range of EVs or the need to recharge them. The electric vehicle routing problem (EVRP) is a more complex problem that takes these factors into account. This study proposes a new algorithm for solving the EVRP, called GeneAnts. GeneAnts is a hybrid algorithm that combines the strengths of genetic algorithm (GA) and ant colony optimization (ACO). GA is a powerful tool for finding solutions and has the ability to escape the local optimal, but it can be slow to find the good solution. ACO is a more efficient algorithm to find the good solution, but it is easy to fall to local optimal solutions. GeneAnts combines the strengths of GA and ACO by using ACO to search for good solutions to the EVRP, and then using GA to improve those solutions. The results of the experiments show that GeneAnts is able to find good solutions to the EVRP more efficiently than GA and ACO. However, GeneAnts uses computational time to find solutions more than GA and ACO.