An Investigation of Parameter Tuning in the Random Adaptive Grouping Algorithm for LSGO Problems

Evgenii Sopov, Alexey Vakhnin

2018

Abstract

Large-scale global optimization (LSGO) is known as one of the most challenging problem for many search algorithms. Many well-known real-world LSGO problems are not separable and are complex for comprehensive analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution with problem decomposition using grouping methods. The random adaptive grouping algorithm (RAG) combines the ideas of random dynamic grouping and learning dynamic grouping. In our previous studies, we have demonstrated that cooperative coevolution (CC) of the Self-adaptive Differential Evolution (DE) with Neighborhood Search (SaNSDE) with RAG (DECC-RAG) outperforms some state-of-the-art LSGO algorithms on the LSGO benchmarks proposed within the IEEE CEC 2010 and 2013. Nevertheless, the performance of the RAG algorithm can be improved by tuning the number of subcomponents. Moreover, there is a hypothesis that the number of subcomponents should vary during the run. In this study, we have performed an experimental analysis of parameter tuning in the RAG. The results show that the algorithm performs better when using subcomponents of larger size. In addition, some improvement can be done by applying dynamic group sizing.

Paper Citation

in Harvard Style

Sopov E. and Vakhnin A. (2018). An Investigation of Parameter Tuning in the Random Adaptive Grouping Algorithm for LSGO Problems. In Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI; ISBN 978-989-758-327-8, SciTePress, pages 255-263. DOI: 10.5220/0006959802550263

in Bibtex Style

@conference{ijcci18,
author={Evgenii Sopov and Alexey Vakhnin},
title={An Investigation of Parameter Tuning in the Random Adaptive Grouping Algorithm for LSGO Problems},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI},
year={2018},
pages={255-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006959802550263},
isbn={978-989-758-327-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI
TI - An Investigation of Parameter Tuning in the Random Adaptive Grouping Algorithm for LSGO Problems
SN - 978-989-758-327-8
AU - Sopov E.
AU - Vakhnin A.
PY - 2018
SP - 255
EP - 263
DO - 10.5220/0006959802550263
PB - SciTePress