Application of ACO to Vehicle Routing Problems Using Three Strategies

DOI

10.2991/lemcs-15.2015.277How to use a DOI?

Keywords

Ant colony optimization; Maximum and minimum ant system; Combinatorial optimization problems; Adaptive; Elite ants

Abstract

Ant Colony Optimization algorithm (ACO) is a viable method for attacking hard combinatorial optimization problems. In this paper, researchers applied adaptive method to the Max-Min ant system (MMAS). In every cycle of the search, researchers can determine the number of elite ants adaptively. In the search process, as long as the ant reached a certain requirement, researchers can think of them as the elite ants, and their pheromone is maintained. When updating the pheromone of path, researchers considered the distance between two points, rather than a uniform release of the pheromone in the path. Punishment and reward system was proposed. Based on the comparison the current path of the ants walked and the optimal path of last circle, the ants were grading. Ant pheromone on the first paths were strengthen, and those on the last paths and weaken. Experiments showed that, in most cases, our algorithm could find the better solution.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yunpeng Wu
AU  - Yonggang Zhang
AU  - Ying Xin
PY  - 2015/07
DA  - 2015/07
TI  - Application of ACO to Vehicle Routing Problems Using Three Strategies
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 1392
EP  - 1396
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.277
DO  - 10.2991/lemcs-15.2015.277
ID  - Wu2015/07
ER  -