Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP
We investigate strategies for pheromone modification of ant algorithms in reaction to the insertion/deletion of a city of Traveling Salesperson Problem (TSP) instances. Three strategies for pheromone diversification through equalization of the pheromone values on the edges are proposed and compared. One strategy acts globally without consideration of the position of the inserted/deleted city. The other strategies perform pheromone modification only in the neighborhood of the inserted/deleted city, where neighborhood is defined differently for the two strategies. We furthermore evaluate different parameter settings for each of the strategies.
KeywordsProblem Instance Travel Salesman Problem Heuristic Information Good Solution Quality Travel Salesperson Problem
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- 2.M. Dorigo, G. Di Caro, “The ant colony optimization meta-heuristic”, in D. Corne, M. Dorigo, F. Glover (Eds.), New Ideas in Optimization, McGraw-Hill, 11–32, 1999.Google Scholar
- 4.R. Schoonderwoerd, O. Holland, J. Bruten, L. Rothkrantz, “Ant-based Load Balancing in Telecommunications Networks,” Adaptive Behavior, 1996.Google Scholar
- 6.M. Dorigo, “Optimization, Learning and Natural Algorithms (in Italian), ” PhD Thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, pp.140, 1992.Google Scholar
- 7.M. Dorigo, L.M. Gambardella, “Ant-Q: A Reinforcement Learning approach to the traveling salesman problem,” Proceedings of ML-95, Twelfth Intern. Conf. on Machine Learning, Morgan Kaufmann, 252–260, 1995.Google Scholar
- 8.M. Dorigo, and L.M. Gambardella, “Ant colony system: A cooperative learning approach to the travelling salesman problem,” IEEE TEC, 1: 53–66, 1997.Google Scholar
- 10.T. Stützle, H. Hoos, “Improvements on the ant system: Introducing MAX(MIN) ant system,” in G.D. Smith et al. (Eds.), Proc. of the International Conf. on Artificial Neutral Networks and Genetic Algorithms, Springer-Verlag, 245–249, 1997.Google Scholar
- 14.D. Merkle, M. Middendorf, H. Schmeck, “Ant Colony Optimization for Resource-Constrained Project Scheduling,” Proc. GECCO-2000, 893–900, 2000.Google Scholar