Abstract
This paper proposes a wasp swarm optimization algorithm, which is applied to the dynamic variant of the maximum satisfiability problem, or MAX-SAT. Here, we describe the changes implemented to optimize the dynamic problem and analyze the parameters of the new algorithm. Wasp swarm optimization accomplishes very well the task of adapting to systematic changes of dynamic MAX-SAT instances derived from static problems, and significantly outperforms the local search algorithm used as benchmark.
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References
Cicirello, V.A., Smith, S.F.: Wasp-like agents for distributed factory coordination. Autonomous Agents and Multi-agent systems 8, 237–266 (2004)
Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2004)
Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)
Garey, M.R., Johson, D.S.J.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)
Hoos, H.H., O’Neill, K.: Stochastic local search methods for dynamic SAT - an initial investigation. In: AAAI-2000 Workshop ”Leveraging Probability and Uncertainty in Computation”, Austin, Texas, pp. 22–26 (2000)
Theraulaz, G., Goss, S., Gervet, J., Deneubourg, J.L.: Task differentiation in polistes wasps colonies: A model for self-organizing groups of robots. In: From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pp. 346–355. MIT Press, Cambridge (1991)
Battiti, R., Protasi, M.: Reactive search, a history-based heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics 2 (1997)
Roli, A., Blum, C., Dorigo, M.: ACO for maximal constraint satisfaction problems. In: MIC’2001 - Metaheuristics International Conference, Porto, Portugal, pp. 187–192 (2001)
Hoos, H.H., Stützle, T.: Local search algorithms for SAT: an empirical evaluation. Journal of Automated Reasoning, special Issue ”SAT 2000”, 421–481 (1999)
Selman, B., Kautz, H., Cohen, B.: Local search strategies for satisfiability testing. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26, pp. 521–532 (1996)
Smyth, K., Hoos, H.H., Stützle, T.: Iterated robust tabu search for MAX-SAT. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 129–144. Springer, Heidelberg (2003)
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Pinto, P.C., Runkler, T.A., Sousa, J.M.C. (2007). Wasp Swarm Algorithm for Dynamic MAX-SAT Problems. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_39
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DOI: https://doi.org/10.1007/978-3-540-71618-1_39
Publisher Name: Springer, Berlin, Heidelberg
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