Abstract
Constraints Satisfaction Problems (CSPs) are known to be hard to solve and require a backtrack search algorithm with exponential time cost. Metaheuristics have recently gained much reputation for solving complex problems and can be employed as an alternative to tackle CSPs even if, in theory, they do not guarantee a complete solution to the problem. This paper proposes a new Discrete Firefly Algorithm (DFA) and investigates its applicability for dealing with CSPs. To assess the performance of the proposed DFA, experiments have been conducted on CSP instances, randomly generated based on the Model RB. The results of the experiments clearly demonstrate the significant performance of the proposed method in dealing with CSPs. For all the instances tested, DFA is successful to find a complete solution that satisfies all constraints in a reasonable amount of time.
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References
Dechter, R.: Constraint Processing. Morgan Kaufmann, San Francisco (2003)
Eiben, Á.E., Van Der Hauw, J.K., van Hemert, J.I.: Graph coloring with adaptive evolutionary algorithms. J. Heuristics 4(1), 25–46 (1998)
Brailsford, S.C., Potts, C.N., Smith, B.M.: Constraint satisfaction problems: algorithms and applications. Eur. J. Oper. Res. 119(3), 557–581 (1999)
Cagnina, L.C., Esquivel, S.C., Coello Coello, C.A.: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32(3), 319–326 (2008)
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver press, Bristol (2010)
Gandomi, A.H.: Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans. 53(4), 1168–1183 (2014)
Abbasian, R., Mouhoub, M.: A new parallel ga-based method for constraint satisfaction problems. Int. J. Comput. Intell. Appl. 15(03), 1650017 (2016)
Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Trans. Evol. Comput. 6(4), 347–357 (2002)
Clerc, M.: Discrete particle swarm optimization: a fuzzy combinatorial black box, 31 May 2006 (2000). http://clerc.maurice.free.fr/PSO
Xu, K., Li, W.: Exact phase transitions in random constraint satisfaction problems. J. Artif. Intell. Res. (JAIR) 12, 93–103 (2000)
Li, M., X, Chen, Li, X., Ma, B., Vitányi, P.M.B.: The similarity metric. IEEE Trans. Inf. Theory 50(12), 3250–3264 (2004)
Durkota, K.: Implementation of a discrete Firefly algorithm for the QAP problem within the sage framework. Bachelor thesis, Czech Technical University (2011)
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Bidar, M., Mouhoub, M., Sadaoui, S., Bidar, M. (2018). Solving Constraint Satisfaction Problems Using Firefly Algorithms. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_22
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DOI: https://doi.org/10.1007/978-3-319-89656-4_22
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