Abstract
Multi colony ant algorithms are evolutionary optimization heuristics that are well suited for parallel execution. Information exchange between the colonies is an important topic that not only influences the parallel execution time but also the optimization behaviour. In this paper different kinds of information exchange strategies in multi colony ant algorithms are investigated. It is shown that the exchange of only a small amount of information can be advantageous not only for a short running time but also to obtain solutions of high quality. This allows the colonies to profit from the good solutions found by other colonies and also to search in different regions of the search space by using different pheromone matrices.
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References
M. Bolondi, M. Bondaza: Parallelizzazione di un algoritmo per la risoluzione del problema del comesso viaggiatore; Master’s thesis, Politecnico di Milano, 1993.
B. Bullnheimer, R.F. Hartl, C. Strauss: A New Rank Based Version of the Ant System-A Computational Study; CEJOR, Vol 7, 25–38, 1999.
B. Bullnheimer, G. Kotsis, C. Strauss: Parallelization Strategies for the Ant System; in: R. De Leone et al. (Eds.), High Performance Algorithms and Software in Nonlinear Optimization; series: Applied Optimization, Vol. 24, Kluwer, 87–100, 1998.
M. Dorigo: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, 1992.
M. Dorigo: Parallel ant system: An experimental study; Unpub. manuscript, 1993.
M. Dorigo, V. Maniezzo, A. Colorni: The Ant System: Optimization by a Colony of Cooperating Agents; IEEE Trans. Sys., Man, Cybernetics — B, 26, 29–41, 1996.
L. M. Gambardella, M. Dorigo: 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.
U. Kohlmorgen, H. Schmeck, K. Haase: Experiences with fine-grained parallel genetic algorithms; Ann. Oper. Res., 90, 203–219, 1999.
F. Krüger, M. Middendorf, D. Merkle: Studies on a Parallel Ant System for the BSP Model; Unpub. manuscript, 1998.
R. Michels, M. Middendorf: An Ant System for the Shortest Common Supersequence Problem; in: D. Corne, M. Dorigo, F. Glover (Eds.), New Ideas in Optimization, McGraw-Hill, 1999, 51–61.
T. Stützle: Parallelization strategies for ant colony optimization; in: A. E. Eiben, T. Bäck, M. Schonauer, H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature-PPSN V, Springer-Verlag, LNCS 1498, 722–731, 1998.
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.
E-G. Talbi, O. Roux, C. Fonlupt, D. Robillard: Parallel ant colonies for combinatorial optimization problems; in J. Rolim et al. (Eds.) Parallel and Distributed Processing, 11 IPPS/SPDP≐99 Workshops, LNCS 1586, Springer, 239–247, 1999. http://www.iwr.uni-heidelberg.de/iwr/comopt/soft/TSPLIB/TSPLIB.html
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Middendorf, M., Reischle, F., Schmeck, H. (2000). Information Exchange in Multi Colony Ant Algorithms. In: Rolim, J. (eds) Parallel and Distributed Processing. IPDPS 2000. Lecture Notes in Computer Science, vol 1800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45591-4_87
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DOI: https://doi.org/10.1007/3-540-45591-4_87
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