Ant Colony Solving Multiple Constraints Problem: Vehicle Route Allocation
Keywords:Ant Colony Optimisation, Vehicle Route Allocation Problem, Multi- Agent Systems
AbstractAnt colonies are successfully used nowadays as multi-agent systems (MAS) to solve difficult optimization problems such as travelling salesman (TSP), quadratic assignment (QAP), vehicle routing (VRP), graph coloring and satisfiability problem. The objective of the research presented in this paper is to adapt an improved version of Ant Colony Optimisation (ACO) algorithm, mainly: the Elitist Ant System (EAS) algorithm in order to solve the Vehicle Route Allocation Problem (VRAP). After a brief introduction in the first section about MAS and their characteristics, the paper presents the rationale within the second section where ACO algorithm and its common extensions are described. In the approach (the third section) are explained the steps that must be followed in order to adapt EAS for solving the VRAP. The resulted algorithm is illustrated in the fourth section. Section five closes the paper presenting the conclusions and intentions.
BÄƒrbat B.E, Moiceanu A., PleÂ¸sca S., Negulescu S.C. (2007).Affordability and Paradigms in Agent- Based Systems. Computer Sc. J. of Moldova, 15, 2(44), pp.178-201.
BÄƒrbat B.E., Negulescu S.C. (2006). From Algorithms to (Sub-)Symbolic Inferences in Multi- Agent Systems. International Journal of Computers, Communications and Control, 1, 3, pp.5-12. http://dx.doi.org/10.15837/ijccc.2006.3.2290
BÄƒrbat B.E., Negulescu S.C., Zamfirescu C.B. (2005). Human-Driven Stigmergic Control. Moving the Threshold. In N. Simonov (Ed.), Proc. of the 17th IMACS World Congress (Scientific Compu- tation, Applied Mathematics and Simulation), pp.86-92. Paris: ISBN 2- 915913-02-01.
Bonabeau E., Dorigo M., Theraulaz G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.
Dorigo M., Maniezzo V., Colorni A. (1996). The Ant System: Optimisation by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26 (1), pp.29-42. http://dx.doi.org/10.1109/3477.484436
Gambardella L.M., Di Caro G. (2005). The Ant Colony Optimization (ACO) Metaheuristic: a Swarm Intelligence Framework for Complex Optimization Tasks. Retrived 2008, from University of Bologna: First Summer School on Aspects of Complexity. Web site: http://www.cs.unibo.it/Ëœfioretti/AC/AC2005/docs/slides_dicaro.pdf
Gambardella L.M., Dorigo M. (1995). Ant-Q: A Reinforcement Learning Approach to the Travelling Salesman Problem. In A. Prieditis and S. Russell (Ed.), Proceedings of the Eleventh Interna- tional Conference on Machine Learning (pp.252-260). San Francisco, CA: Morgan Kaufmann. http://dx.doi.org/10.1016/b978-1-55860-377-6.50039-6
Negulescu S.C., BÄƒrbat B.E. (2004). Enhancing the effectiveness of simple multi-agent systems through stigmergic coordination. In ICSC-NAISO (Ed.), Fourth International ICSC Symposium on ENGINEERING OF INTELLIGENT SYSTEMS (EIS 2004), pp.149-156. Canada: ICSC-NAISO Academic Press.
Negulescu S.C., Zamfirescu C.B., BË˜arbat B.E. (2006). User-Driven Heuristics for nondeterministic problems. Studies in Informatics and Control (Special issue dedicated to the 2nd Romanian- Hungarian Joint Symp. on Applied Computational Intelligence), 15, 3, pp.289-296.
Stützle T., Hoos H.H. (2000). MAX-MIN Ant System. Future Generation Computer Systems, 16(8), pp.889-914.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.