Title: Solving route optimisation problem in logistics distribution through an improved ant colony optimisation algorithm
Authors: Gailian Zhang
Addresses: School of Applied Technology, The North Teaching Area of Xi'an International University, Fuyu Lu, Yanta District, Xi'an, Shaanxi, China
Abstract: In this paper, aiming at conventional Ant Colony algorithm's defects and shortcomings, we introduce Genetic Algorithm to improve it. By the GA's reproduction, crossover and mutation operators, the ACA's convergence rate and global searching ability have a significant improvement. Besides, we improve the updating mode of pheromone to enhance the adaptability of ants, the ACA can automatic adjust pheromone residual degree when executing the algorithm for convergence. Besides, introducing a new deterministic searching method will accelerate the heuristic searching method rate. After the description of our improved algorithm, we do two groups of experiments, the results show that our proposed algorithm has a good effect on solving logistics distribution routing optimisation problem, compared with the conventional algorithm, our experiments are on large logistics distribution route sets, the results show that our improved algorithm can get the optimal solution rapidly and accurately, the results are more robust than conventional results.
Keywords: ant colony optimisation; improved ACO; pheromone; deterministic searching; genetic factor; genetic algorithms; routing optimisation; logistics distribution; swarm intelligence; metaheuristics.
International Journal of Services Operations and Informatics, 2017 Vol.8 No.3, pp.218 - 230
Available online: 10 Jan 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article