Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
A Parameter-Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks

Authors: Felix Dobslaw

Abstract:

In this paper, a framework for the simplification and standardization of metaheuristic related parameter-tuning by applying a four phase methodology, utilizing Design of Experiments and Artificial Neural Networks, is presented. Metaheuristics are multipurpose problem solvers that are utilized on computational optimization problems for which no efficient problem specific algorithm exist. Their successful application to concrete problems requires the finding of a good initial parameter setting, which is a tedious and time consuming task. Recent research reveals the lack of approach when it comes to this so called parameter-tuning process. In the majority of publications, researchers do have a weak motivation for their respective choices, if any. Because initial parameter settings have a significant impact on the solutions quality, this course of action could lead to suboptimal experimental results, and thereby a fraudulent basis for the drawing of conclusions.

Keywords: Parameter-Tuning, Metaheuristics, Design of Experiments, Artificial Neural Networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058561

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1374

References:


[1] E. Eiben, R. Hinterding, and Z. Michalewicz, "Parameter control in evolutionary algorithms," IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124-141, 1999.
[2] N. Figlali, C. Özkale, O. Engin, and A. Figlali, "Investigation of Ant System parameter interactions by using design of experiments for job-shop scheduling problems," Computers & Industrial Engineering, vol. 56, pp. 538-559, 2009.
[3] G. Tewolde, D. Hanna, and R. Haskell, "Enhancing performance of pso with automatic parameter tuning technique," pp. 67-73, 2009.
[4] Y. Cooren, M. Clerc, and P. Siarry, "Performance evaluation of tribes, an adaptive particle swarm optimization algorithm," Swarm Intelligence, vol. 3, no. 2, pp. 149-178, 2009.
[5] K. Wong and Komarudin, "Parameter tuning for ant colony optimization: A review," pp. 542-545, 2008.
[6] L. Eriksson, E. Johansson, C. Kettaneh-Wold, and S. Wikström, Wold, Design of Experiments, Principles and Applications. MKS Umetrics AB, 2008.
[7] O. Kramer, B. Gloger, and A. Goebels, "An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments," pp. 674-681, 2007.
[8] Y. Lucas, A. Domingues, D. Driouchi, and S. Treuillet, "Design of experiments for performance evaluation and parameter tuning of a road image processing chain," Eurasip Journal on Applied Signal Processing, 2006.
[9] G. Zhang, B. Eddy Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, vol. 14, no. 1, pp. 35-62, 1998.
[10] K.-P. Wang, L. Huang, C.-G. Zhou, and W. Pang, "Particle swarm optimization for traveling salesman problem," vol. 3, pp. 1583-1585, 2003.