A comparative study of fuzzy parameter control in a general purpose local search metaheuristic

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.

[1]  Katja Verbeeck,et al.  A New Learning Hyper-heuristic for the Traveling Tournament Problem , 2009 .

[2]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[3]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[4]  Dong-ping Tian,et al.  Fuzzy Particle Swarm Optimization Algorithm , 2009, 2009 International Joint Conference on Artificial Intelligence.

[5]  Ender Özcan,et al.  An iterated multi-stage selection hyper-heuristic , 2016, Eur. J. Oper. Res..

[6]  Ender Özcan,et al.  Hill Climbers and Mutational Heuristics in Hyperheuristics , 2006, PPSN.

[7]  Francisco Herrera,et al.  Adaptic Control of the Mutation Probability by Fuzzy Logic Controllers , 2000, PPSN.

[8]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[9]  Juebang Yu,et al.  Fuzzy tabu search for solving the assignment problem , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.

[10]  Hou-Jun Wang,et al.  Fuzzy tabu search method for the clustering problem , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[11]  Juan Rada-Vilela.  fuzzylite a fuzzy logic control library in C + + , 2013 .

[12]  Sanja Petrovic,et al.  The Cross-Domain Heuristic Search Challenge - An International Research Competition , 2011, LION.

[13]  A A Alsawy,et al.  Fuzzy-based ant colony optimization algorithm , 2010, 2010 2nd International Conference on Computer Technology and Development.

[14]  Robert Ivor John,et al.  Fuzzy adaptive parameter control of a late acceptance hyper-heuristic , 2014, 2014 14th UK Workshop on Computational Intelligence (UKCI).

[15]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[16]  G. Dueck.  New optimization heuristics , 1993 .

[17]  Dušan Teodorović,et al.  Application of fuzzy sets theory to the saving based vehicle routing algorithm , 1991 .

[18]  Sanja Petrovic,et al.  HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search , 2011, EvoCOP.