ABSTRACT
The Markov chain Monte Carlo paradigm has developed powerful and elegant techniques for estimating the time until a Markov chain approaches a stationary distribution. This time is known as mixing time. We introduce the reader into mixing time estimations via coupling arguments and use the mixing of pheromone models for analyzing the expected optimization time of ant colony optimization. We demonstrate the approach for plateaus in pseudo-Boolean optimization and derive upper bounds for the time until a target set is found. We also describe how mixing times can be estimated for MMAS ant systems on shortest path problems.
- D. Aldous and J. Fill. Reversible Markov Chains and Random Walks on Graphs. Monograph in preparation, http://stat-www.berkeley.edu/users/aldous/RWG/book.html, 2010.Google Scholar
- N. Attiratanasunthron and J. Fakcharoenphol. A running time analysis of an ant colony optimization algorithm for shortest paths in directed acyclic graphs. Information Processing Letters, 105 (3): 88--92, 2008. Google ScholarDigital Library
- B. Doerr and D. Johannsen. Refined runtime analysis of a basic ant colony optimization algorithm. In Proceedings of the Congress of Evolutionary Computation (CEC '07), pages 501--507. IEEE Press, 2007.Google ScholarCross Ref
- B. Doerr, M. Gnewuch, N. Hebbinghaus, and F. Neumann. A rigorous view on neutrality. In Proceedings of the Congress of Evolutionary Computation (CEC '07), pages 2591--2597. IEEE Press, 2007.Google ScholarCross Ref
- B. Doerr, F. Neumann, D. Sudholt, and C. Witt. On the runtime analysis of the 1-ANT ACO algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '07), pages 33--40. ACM, 2007. Google ScholarDigital Library
- M. Dorigo and C. Blum. Ant colony optimization theory: A survey. Theoretical Computer Science, 344: 243--278, 2005. Google ScholarDigital Library
- M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, 2004. Google ScholarDigital Library
- S. Droste, T. Jansen, and I. Wegener. Upper and lower bounds for randomized search heuristics in black-box optimization. Theory of Computing Systems, 39 (4): 525--544, 2006. Google ScholarDigital Library
- J. Garnier, L. Kallel, and M. Schoenauer. Rigorous hitting times for binary mutations. Evolutionary Computation, 7 (2): 173--203, 1999. Google ScholarDigital Library
- W. J. Gutjahr. First steps to the runtime complexity analysis of ant colony optimization. Computers and Operations Research, 35 (9): 2711--2727, 2008. Google ScholarDigital Library
- W. J. Gutjahr and G. Sebastiani. Runtime analysis of ant colony optimization with best-so-far reinforcement. Methodology and Computing in Applied Probability, 10: 409--433, 2008.Google ScholarDigital Library
- B. Hajek. Hitting-time and occupation-time bounds implied by drift analysis with applications. Advances in Applied Probability, 14: 502--525, 1982.Google ScholarCross Ref
- E. Happ, D. Johannsen, C. Klein, and F. Neumann. Rigorous analyses of fitness-proportional selection for optimizing linear functions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '08), pages 953--960. ACM, 2008. Google ScholarDigital Library
- T. P. Hayes, J. C. Vera, and E. Vigoda. Randomly coloring planar graphs with fewer colors than the maximum degree. In STOC '07: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 450--458. ACM, 2007. Google ScholarDigital Library
- C. Horoba and D. Sudholt. Running time analysis of ACO systems for shortest path problems. In Proceedings of Engineering Stochastic Local Search Algorithms (SLS '09), volume 5752 of LNCS, pages 76--91. Springer, 2009. Google ScholarDigital Library
- C. Horoba and D. Sudholt. Ant colony optimization for stochastic shortest path problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 1465--1472, 2010. Google ScholarDigital Library
- M. Jerrum and A. Sinclair. The Markov chain Monte Carlo method: an approach to approximate counting and integration. In Approximation Algorithms for NP-hard Problems, pages 482--520. PWS Publishing, 1996. Google ScholarDigital Library
- M. Jerrum, A. Sinclair, and E. Vigoda. A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries. Journal of the ACM, 51 (4): 671--697, 2004. Google ScholarDigital Library
- J. Jägersküpper and T. Storch. When the plus strategy outperforms the comma strategy - and when not. In Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence (FOCI '07), pages 25--32. IEEE, 2007.Google ScholarCross Ref
- T. Kötzing, P. K. Lehre, P. S. Oliveto, and F. Neumann. Ant colony optimization and the minimum cut problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 1393--1400. ACM, 2010. Google ScholarDigital Library
- T. Kötzing, F. Neumann, H. Röglin, and C. Witt. Theoretical properties of two ACO approaches for the traveling salesman problem. In Seventh International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS '10), volume 6234 of LNCS, pages 324--335. Springer, 2010. Google ScholarDigital Library
- J. Lässig and D. Sudholt. The benefit of migration in parallel evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 1105--1112, 2010. Google ScholarDigital Library
- P. K. Lehre and X. Yao. On the impact of the mutation-selection balance on the runtime of evolutionary algorithms. In FOGA '09: Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms, pages 47--58. ACM, 2009. Google ScholarDigital Library
- D. A. Levin, Y. Peres, and E. L. Wilmer. Markov Chains and Mixing Times. American Mathematical Society, 2008.Google ScholarCross Ref
- J. Liu. Monte Carlo Strategies in Scientific Computing. Springer, 2002. Google ScholarDigital Library
- L. Lovász and S. Vempala. Simulated annealing in convex bodies and an O*(n4) volume algorithm. Journal of Computer and System Sciences, 72 (2): 392--417, 2006. Google ScholarDigital Library
- B. Mitavskiy, J. E. Rowe, A. H. Wright, and L. M. Schmitt. Quotients of Markov chains and asymptotic properties of the stationary distribution of the Markov chain associated to an evolutionary algorithm. Genetic Programming and Evolvable Machines, 9 (2): 109--123, 2008. Google ScholarDigital Library
- M. Mitzenmacher and E. Upfal. Probability and Computing. Cambridge University Press, 2005.Google ScholarDigital Library
- F. Neumann and I. Wegener. Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theoretical Computer Science, 378 (1): 32--40, 2007. Google ScholarDigital Library
- F. Neumann and C. Witt. Runtime analysis of a simple ant colony optimization algorithm. In Proceedings of the 17th International Symposium on Algorithms and Computation (ISAAC '06), volume 4288 of LNCS, pages 618--627. Springer, 2006. Google ScholarDigital Library
- F. Neumann and C. Witt. Ant Colony Optimization and the minimum spanning tree problem. In Proceedings of Learning and Intelligent Optimization (LION '07), volume 5313 of LNCS, pages 153--166. Springer, 2008. Google ScholarDigital Library
- F. Neumann and C. Witt. Runtime analysis of a simple ant colony optimization algorithm. Algorithmica, 54 (2): 243--255, 2009. Google ScholarDigital Library
- F. Neumann, D. Sudholt, and C. Witt. Comparing variants of MMAS ACO algorithms on pseudo-Boolean functions. In Proceedings of Engineering Stochastic Local Search Algorithms (SLS '07), volume 4638 of LNCS, pages 61--75. Springer, 2007. Google ScholarDigital Library
- F. Neumann, D. Sudholt, and C. Witt. Rigorous analyses for the combination of ant colony optimization and local search. In Proceedings of the Sixth International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS '08), volume 5217 of LNCS, pages 132--143. Springer, 2008. Google ScholarDigital Library
- F. Neumann, P. S. Oliveto, and C. Witt. Theoretical analysis of fitness-proportional selection: landscapes and efficiency. In GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 835--842. ACM, 2009. Google ScholarDigital Library
- F. Neumann, D. Sudholt, and C. Witt. Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intelligence, 3 (1): 35--68, 2009.Google ScholarCross Ref
- F. Neumann, D. Sudholt, and C. Witt. A few ants are enough: ACO with iteration-best update. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 63--70. ACM, 2010. Google ScholarDigital Library
- S. Sanyal, R. S., and S. Biswas. Necessary and sufficient conditions for success of the metropolis algorithm for optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 1417--1424. ACM, 2010. Google ScholarDigital Library
- C. Witt. Rigorous runtime analysis of swarm intelligence algorithms - an overview. In Swarm Intelligence for Multi-objective Problems in Data Mining, volume 242/2009 of Studies in Computational Intelligence, pages 157--177. Springer, 2009.Google ScholarCross Ref
- Y. Zhou. Runtime analysis of an ant colony optimization algorithm for TSP instances. IEEE Transactions on Evolutionary Computation, 13 (5): 1083--1092, 2009. Google ScholarDigital Library
Index Terms
- Using markov-chain mixing time estimates for the analysis of ant colony optimization
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