Abstract
Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.
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L. van Laarhoven and E. Aarts, Simulated Annealing: Theory and Applications, D Reidel Publishing Company: Dordecht, 1987, p. 186.
F. Glover, and M. Laguna, Tabu Search, Kluwer Academic Publishers: Boston, MA, 1997, p. 442.
D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley: Reading, MA, 1989, p. 412.
M. Dorigo and G. Di Caro, “The ant colony optimization meta-heuristic,” in New Ideas in Optimization, edited by D. Corne, M. Dorigo, and F. Golver, McGraw-Hill, 1999, pp. 11–32.
M. Dorigo, “Optimization, learning and natural algorithms, PhD Thesis,” Dipartimento di Elettronica, Politechico di Milano, Italy, 1992.
M. Dorigo and L. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computing, vol. 1, pp. 53–66, 1997.
M Dorigo and L. Gambardella, “Ant colonies for the traveling salesman problem,” Biosystems, vol. 43, pp. 73–81, 1997.
M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man and Cybernetics—Part B, vol. 26, pp. 29–41, 1996.
T. Stützle and M. Dorigo, “ACO algorithms for the traveling salesman problem,” in Evolutionary Algorithms in Engineering and Computer Science, edited by K. Miettinen, M. Makela, P. Neittaanmaki, and J. Periaux, Wiley.
G. Reinelt TSPLIB95. Available from http://www.iwr.uni-heidelberg.de/iwr/comopt/soft/TSPLIB95/TSPLIB95.html
D. Angus and T. Hendtlass, “Ant colony optimization applied to dynamically changing problem,” in Developments in Applied Artificial Intelligence. LNAI2358, Springer, 2002, pp. 618–627.
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Angus, D., Hendtlass, T. Dynamic Ant Colony Optimisation. Appl Intell 23, 33–38 (2005). https://doi.org/10.1007/s10489-005-2370-8
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DOI: https://doi.org/10.1007/s10489-005-2370-8