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Solving route optimisation problem in logistics distribution through an improved ant colony optimisation algorithm

Solving route optimisation problem in logistics distribution through an improved ant colony... 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 algorithm; pheromone; deterministic searching; genetic factor; genetic algorithm. Reference to this paper should be made as follows: Zhang, G. (2017) ` through an improved ant colony optimisation algorithm', Int. J. Services Operations and Informatics, Vol. 8, No. 3, pp.218­230. Biographical notes: Gailian Zhang is Lecturer at http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

Solving route optimisation problem in logistics distribution through an improved ant colony optimisation algorithm

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Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2017.081511
Publisher site
See Article on Publisher Site

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 algorithm; pheromone; deterministic searching; genetic factor; genetic algorithm. Reference to this paper should be made as follows: Zhang, G. (2017) ` through an improved ant colony optimisation algorithm', Int. J. Services Operations and Informatics, Vol. 8, No. 3, pp.218­230. Biographical notes: Gailian Zhang is Lecturer at

Journal

International Journal of Services Operations and InformaticsInderscience Publishers

Published: Jan 1, 2017

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