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There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of parameters to be configured. Fuzzy logic has been used extensively in control applications and is known for its ability to handle uncertainty. In this study, we investigate the potential of using fuzzy systems to control the parameter settings of a threshold accepting (TA) metaheuristic for improving the overall effectiveness of a cross-domain approach. We have evaluated the performance of various general purpose local search metaheuristics which mix multiple heuristics at random and apply the TA metaheuristic with fixed threshold, crisp (non-fuzzy) rule-based control of the threshold and various fuzzy systems controlling the threshold. The empirical results show that the approach using the TA with crisp rule-based control performs the best across six problem domains from a benchmark.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. R. Garey and D. S. Johnson, Computers and Intractability; A Guide to the Theory of NP-Completeness. New York, NY, USA: W. H. Freeman & Co., 1990.
    • [2] G. Ochoa and M. Hyde. The cross-domain heuristic search challenge (chesc 2011). [Online]. Available: http://www.asap.cs.nott.ac. uk/chesc2011/
    • [3] E. O¨zcan, B. Bilgin, and E. E. Korkmaz, “Hill climbers and mutational heuristics in hyperheuristics,” in Parallel Problem Solving from Nature - PPSN IX, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006, vol. 4193, pp. 202-211.
    • [4] A. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” Evolutionary Computation, IEEE Transactions on, vol. 3, no. 2, pp. 124-141, Jul 1999.
    • [5] L. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338 - 353, 1965.
    • [6] F. Herrera and M. Lozano, “Adaptive control of the mutation probability by fuzzy logic controllers,” in Parallel Problem Solving from Nature PPSN VI, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000, vol. 1917, pp. 335-344.
    • [7] Y. Shi and R. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, vol. 1, 2001, pp. 101-106 vol. 1.
    • [8] D.-P. Tian and N.-Q. Li, “Fuzzy particle swarm optimization algorithm,” in Artificial Intelligence, 2009. JCAI '09. International Joint Conference on, 2009, pp. 263-267.
    • [9] A. Alsawy and H. Hefny, “Fuzzy-based ant colony optimization algorithm,” in Computer Technology and Development (ICCTD), 2010 2nd International Conference on, 2010, pp. 530-534.
    • [10] C. Li, J. Yu, and X. Liao, “Fuzzy tabu search for solving the assignment problem,” in Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on, vol. 2, 2002, pp. 1151-1155 vol.2.
    • [11] H.-B. Xu, H.-J. Wang, and C.-G. Li, “Fuzzy tabu search method for the clustering problem,” in Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on, vol. 2, 2002, pp. 876- 880 vol.2.
    • [12] W. G. Jackson, E. O¨zcan, and R. I. John, “Fuzzy adaptive parameter control of a late acceptance hyper-heuristic,” in Computational Intelligence (UKCI), 2014 14th UK Workshop on, Sept 2014, pp. 1-8.
    • [13] K. So¨rensen and F. W. Glover, “Metaheuristics,” in Encyclopedia of Operations Research and Management Science, S. Gass and M. C. Fu, Eds. Springer US, 2013, pp. 960-970.
    • [14] G. Dueck and T. Scheuer, “Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing,” Journal of Computational Physics, vol. 90, no. 1, pp. 161 - 175, 1990.
    • [15] G. Dueck, “New optimization heuristics: The great deluge algorithm and the record-to-record travel,” Journal of Computational Physics, vol. 104, no. 1, pp. 86 - 92, 1993.
    • [16] V. K. Mısır M, Wauters T and B. G. V, “A new learning hyperheuristic for the traveling tournament problem,” in Proceedings of the 8th Metaheuristic International Conference (MIC09), 2009.
    • [17] A. Kheiri and E. O¨zcan, “An iterated multi-stage selection hyperheuristic,” European Journal of Operational Research, vol. 250, no. 1, pp. 77 - 90, 2016.
    • [18] G. Ochoa, M. Hyde, T. Curtois, J. A. Vazquez-Rodriguez, J. Walker, M. Gendreau, G. Kendall, B. McCollum, A. J. Parkes, S. Petrovic, and E. K. Burke, “Hyflex: A benchmark framework for cross-domain heuristic search,” in Evolutionary Computation in Combinatorial Optimization, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012, vol. 7245, pp. 136-147.
    • [19] J. Rada-Vilela, “fuzzylite: a fuzzy logic control library,” 2014. [Online]. Available: http://www.fuzzylite.com
    • [20] D. Teodorovic and S. Kikuchi, “Application of fuzzy sets theory to the saving based vehicle routing algorithm,” Civil Engineering Systems, vol. 8, no. 2, pp. 87-93, 1991.
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