A Parameter-Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:52255", author = "Felix Dobslaw", title = "A Parameter-Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks", 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.", volume = "4", number = "4", pages = "670-4", }