A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods

Eduardo B. M. Barbosa, Edson L. F. Senne

Abstract

The fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both in the algorithms performance, as in the quality of solutions. The tuning of metaheuristics requires the use of innovative methodologies, usually interesting to different research communities. In this context, this paper aims to contribute to the literature by presenting a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters, looking for candidate configurations near of a promising alternative, and consistently finds good settings for different metaheuristics. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve a classical task scheduling problem. The results of the proposed approach are compared with results yielded by the same metaheuristics tuned through different strategies, such as the brute-force and racing. Broadly, the proposed method proved to be effective in terms of the overall time of the tuning process. Our results from experimental studies reveal that metaheuristics tuned by means of HORA reach the same good results than when tuned by the other time-consuming fine-tuning approaches. Therefore, from the results presented in this study it is concluded that HORA is a promising and powerful tool for the fine-tuning of different metaheuristics, mainly when the overall time of tuning process is considered.

References

  1. Adeson-Diaz, B., Laguna, M., 2006. Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research, Baltimore, v. 54, n. 1, p. 99-114.
  2. Amoozegar, M.; Rashedi, E., 2014. Parameter tuning of GSA using DOE. In: 4th International Conference on Computer and Knowledge Engineering (ICCKE), 4., 2014, p. 431-436.
  3. Akbaripour, H.; Masehian, E., 2013. Efficient and Robust Parameter Tuning for Heuristic Algorithms. International Journal of Industrial Engineering & Production Research, Tehran, v. 24, n. 2, p. 143-150.
  4. Balaprakash, P., Birattari, M., Stützle, T., Dorigo, M., 2007. Improvement strategies for the F-Race algorithm: sampling design and iterative refinement. In: 4th International Workshop On Hybrid Metaheuristics, 4., 2007, p. 108-122.
  5. Beasley, J. E., 1990. OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society, Oxford, v. 41, n. 11, p. 1069-1072.
  6. Bertsimas, D., Tsitsiklis, J., 1993. Simulated annealing. Statistical Science, Hayward, v. 8, n. 1, p. 10-15.
  7. Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K., 2002. A racing algorithm for configuring metaheuristics. In: Genetic apnd Evolutionary Computation Conference, 2002, New York. p. 11-18.
  8. Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T., 2009. F-Race and iterated F-Race: an overview. Bruxelles: Iridia Technical Report Series. 21 p.
  9. Blum, C., Roli, A., 2003. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys, v. 35, n. 3, p. 268-308.
  10. Calvet, L.; Juan, A. A.; Serrat, C.; Ries, J. 2016. A statistical learning based approach for parameter finetuning of metaheuristics. SORT (Statistics and Operations Research Transactions), v. 40, n. 1, p. 201- 224.
  11. Cerny, V., 1985. Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, v. 45, p. 41-51.
  12. Dobslaw, F., 2010. A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. In: Sixth International Conference On Natural Computation, 6., 2010, Cairo, p. 1-4.
  13. Holland, J. H., 1975. Adaptation in natural and artificial systems. Boston: University of Michigan Press, 211 p.
  14. Hutter, F., Hoos, H., Leyton-Brown, K., Stützle, T., 2009. ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research, v. 36, p. 267-306.
  15. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., 1983. Optimization by simulated annealing. Science, London, v. 220, n. 4598, p. 671-680.
  16. Lessmann, S.; Caserta, M.; Arango, I. M., 2011. Tuning metaheuristics: A data mining based approach for particle swarm optimization. Expert Systems with Applications, v. 38, n. 10, New York: Pergamon Press, p. 12826-12838.
  17. Maron, O., Moore, A. W., 1994. Hoeffding races: accelerating model selection search for classification and function approximation. Advances in Neural Information Processing Systems, San Mateo, p. 59-66.
  18. Montgomery, D. C., 2012. Design and analysis of experiments. 8th ed. New Jersey: John Wiley & Sons Inc.. 699 p.
  19. Neumüller, C.; Wagner, S.; Kronberger, G.; Affenzeller, M., 2011. Parameter Meta-optimization of Metaheuristic Optimization Algorithms. In: 13th International Conference on Computer Aided Systems Theory (EUROCAST 2011), 13., 2011, Las Palmas de Gran Canaria, p. 367-374.
  20. Ries, J.; Beullens, P.; Salt, D., 2012. Instance-specific multi-objective parameter tuning based on fuzzy logic. European Journal of Operational Research, v. 218, p. 305-315.
  21. Schmidt, G., 2000. Scheduling with limited machine availability. European Journal of Operational Research, Amsterdam, v. 121, p. 1-15.
  22. Talbi, E.G., 2003. Metaheuristics: from design to implementation. New Jersey: John Wiley & Sons Inc., 593 p.
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Paper Citation


in Harvard Style

B. M. Barbosa E. and L. F. Senne E. (2017). A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods . In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 203-210. DOI: 10.5220/0006106402030210


in Bibtex Style

@conference{icores17,
author={Eduardo B. M. Barbosa and Edson L. F. Senne},
title={A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2017},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006106402030210},
isbn={978-989-758-218-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods
SN - 978-989-758-218-9
AU - B. M. Barbosa E.
AU - L. F. Senne E.
PY - 2017
SP - 203
EP - 210
DO - 10.5220/0006106402030210