Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System

Miguel Leon, Magnus Evestedt, Ning Xiong

2015

Abstract

Accurate system modelling is an important prerequisite for optimized process control in modern industrial scenarios. The task of parameter identification for a model can be considered as an optimization problem of searching for a set of continuous parameters to minimize the discrepancy between the model outputs and true output values. Differential Evolution (DE), as a class of population-based and global search algorithms, has strong potential to be employed here to solve this problem. Nevertheless, the performance of DE is rather sensitive to its two running parameters: scaling factor and crossover rate. Improper setting of these two parameters may cause weak performance of DE in real applications. This paper presents a new adaptive algorithm for DE, which does not require good parameter values to be specified by users in advance. Our new algorithm is established by integration of greedy search into the original DE algorithm. Greedy search is conducted repeatedly during the running of DE to reach better parameter assignments in the neighborhood. We have applied our adaptive DE algorithm for process model identification in a Furnace Optimized Control System (FOCS). The experiment results revealed that our adaptive DE algorithm yielded process models that estimated temperatures inside a furnace more precisely than those produced by using the original DE algorithm.

References

  1. Bakare, G., Krost, G., Venayagamoorthy, G., and Aliyu, U. (2007). Comparative application of differential evolution and particle swarm techniques to reactive power and voltage control. In International Conference on Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. Toki Messe, Niigata, pages 1- 6.
  2. Gong, W. and Cai, Z. (2014). Parameter optimization of pemfc model with improved multi-strategy adaptive differential evolution. Engineering Applications of Artificial Intelligence, 27:28-40.
  3. Leon, M. and Xiong, N. (2014a). Investigation of mutation strategies in differential evolution for solving global optimization problems. In Artificial Intelligence and Soft Computing, pages 372-383. springer.
  4. Leon, M. and Xiong, N. (2014b). Using random local search helps in avoiding local optimum in diefferential evolution. In Proc. Artificial Intelligence and Applications, AIA2014, Innsbruck, Austria, pages 413-420.
  5. Leon, M. and Xiong, N. (2015). Eager random search for differential evolution in continuous optimization. In Progress in Artificial Intelligence, pages 286-291.
  6. Mohanty, B., Panda, S., and PK, H. (2014). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multisource power system. International journal of electrical power & energy systems, 54:77-85.
  7. Mulumba, T. and Folly, K. (2012). Application of selfadaptive differential evolution to tuning pss parameters. In Power Engineering Society Conference and Exposition in Africa (PowerAfrica), 2012 IEEE, Johannesburg, pages 1-5.
  8. Norberg, P. and Leden, B. (1988). New developments of the computer control system focs-rf - application to the hot strip mill at ssab, domnarvet. SCANHEATING II, pages 31-60.
  9. Sickel, J. V., Lee, K., and Heo, J. (2007). Differential evolution and its applications to power plant control. In International Conference on Intelligent Systems Applications to Power Systems, 2007. ISAP 2007., Toki MEsse, Niigata, pages 1-6. IEEE.
  10. Storn, R. and Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341 - 359.
  11. Xiong, N., Molina, D., Leon, M., and Herrera, F. (2015). A walk into metaheuristics for engineering optimization: Principles, methods, and recent trends. International Journal of Computational Intelligence Systems, 8(4):606-636.
  12. Zou, D., Liu, H., Gao, L., and S.Li (2011). An improved differential evolution algorithm for the task assignment problem. Engineering Applications of Artificial Intelligence, 24:616-624.
Download


Paper Citation


in Harvard Style

Leon M., Evestedt M. and Xiong N. (2015). Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 48-54. DOI: 10.5220/0005591300480054


in Bibtex Style

@conference{ecta15,
author={Miguel Leon and Magnus Evestedt and Ning Xiong},
title={Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={48-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005591300480054},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System
SN - 978-989-758-157-1
AU - Leon M.
AU - Evestedt M.
AU - Xiong N.
PY - 2015
SP - 48
EP - 54
DO - 10.5220/0005591300480054