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

2016

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.

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