The choice of optimal path for power battery distribution based on improved ant colony optimization

摘要

This paper applies the meta-heuristic method of improved ant colony optimization (ACO) to the choice of optimal path for electric vehicles' power battery distribution. The procedure simulates the decision-making processes of ant colonies as they forage for food. It is similar to other adaptive learning and artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. It takes the actual shortest path, traffic congestion and road grade in distribution network into consideration, which can effectively balance the distance and the time. What's more, this paper improves the strategies of pheromone update and the choice of adjacent nodes in traditional ant colony optimization. Experimentation shows that the algorithm is successful in finding optimal path, conforming to the planning line of the actual distribution network.