A Meta-heuristic for Improving the Performance of an Evolutionary Optimization Algorithm Applied to the Dynamic System Identification Problem

Ivan Ryzhikov, Eugene Semenkin, Evgenii Sopov

Abstract

In this paper a meta-heuristic for improving the performance of an evolutionary optimization algorithm is proposed. An evolutionary optimization algorithm is applied to the process of solving an inverse mathematical modelling problem for dynamical systems. The considered problem is related to the complex extremum seeking problem. The objective function and a method of determining a solution perform a class of optimization problems that require specific improvements of optimization algorithms. An investigation of algorithm efficiency revealed the importance of designing and implementing an operator that prevents population stagnation. The proposed meta-heuristic estimates the risk of the algorithm being stacked in a local optimum neighbourhood and it estimates whether the algorithm is close to stagnation areas. The meta-heuristic controls the algorithm and restarts the search if necessary. The current study focuses on increasing the algorithm efficiency by tuning the meta-heuristic settings. The examination shows that implementing the proposed operator sufficiently improves the algorithm performance.

References

  1. Beligiannis G. N., Tsirogiannis G. A., Pintelas P. E., 2004: Restartings: A Technique to Improve Classic Genetic Algorithms' Performance. J. Glob. Optim. 1, pp. 112- 115.
  2. Dao S. D., Abhary K., Marian R. M., 2014: Optimization of partner selection and collaborative transportation scheduling in Virtual Enterprises using GA. Expert Syst. Appl. 41(15), pp. 6701-6717.
  3. Eiben A. E., Smith J., 2015: From evolutionary computation to the evolution of things. Nature, 521(7553), pp. 476-482.
  4. Fukunaga A. S., 1998, Restart Scheduling for Genetic Algorithm. Lecture Notes of Computer Science, Vol. 1498, pp. 357-369.
  5. Loshchilov I., Schoenauer M., Sebag M., 2012: Alternative Restart Strategies for CMA-ES, Lecture Notes in Computer Science, Vol. 7491, pp. 296-305.
  6. Naiborhu J., Firman, Mu'tamar K., 2013. Particle Swarm Optimization in the Exact Linearization Technic for Output Tracking of Non-Minimum Phase Nonlinear Systems. Applied Mathematical Sciences, Vol. 7, no. 109, pp. 5427-5442.
  7. Narwal A., Prasad B. R., 2016. A Novel Order Reduction Approach for LTI Systems Using Cuckoo Search Optimization and Stability Equation. IETE Journal of Research, 62:2, pp. 154-163.
  8. Parmar G., Prasad R., Mukherjee S., 2007. Order reduction of linear dynamic systems using stability equation method and GA. International Journal of computer and Infornation Engeneering 1:1, pp.26-32.
  9. Ryzhikov I., Semenkin E., Panfilov I., 2016. Evolutionary optimization algorithms for differential equation parameters, initial value and order identification. Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 1, pp. 168-176.
  10. Ryzhikov I., Semenkin E., 2013. Evolutionary Strategies Algorithm Based Approaches for the Linear Dynamic System Identification. Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, Volume 7824. - Springer-Verlag, Berlin, Heidelberg, pp. 477-483.
  11. Schwefel Hans-Paul, 1995. Evolution and Optimum Seeking. New York: Wiley & Sons.
  12. Sersic K., Urbiha I., 1999. Parameter Identification Problem Solving Using Genetic Algorithm. Proceedings of the 1. Conference on Applied Mathematics and Computation, pp. 253-261.
  13. Wolpert D.H., Macready, W.G., 1997: No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation, 1, pp. 67-82.
Download


Paper Citation


in Harvard Style

Ryzhikov I., Semenkin E. and Sopov E. (2016). A Meta-heuristic for Improving the Performance of an Evolutionary Optimization Algorithm Applied to the Dynamic System Identification Problem . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 178-185. DOI: 10.5220/0006049601780185


in Bibtex Style

@conference{ecta16,
author={Ivan Ryzhikov and Eugene Semenkin and Evgenii Sopov},
title={A Meta-heuristic for Improving the Performance of an Evolutionary Optimization Algorithm Applied to the Dynamic System Identification Problem},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006049601780185},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - A Meta-heuristic for Improving the Performance of an Evolutionary Optimization Algorithm Applied to the Dynamic System Identification Problem
SN - 978-989-758-201-1
AU - Ryzhikov I.
AU - Semenkin E.
AU - Sopov E.
PY - 2016
SP - 178
EP - 185
DO - 10.5220/0006049601780185