Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization

Mohammad R. Raeesi N., Ziad Kobti

2014

Abstract

Dynamic Heterogeneous Multi-Population Cultural Algorithm (D-HMP-CA) is a novel algorithm to solve global optimization problems. It incorporates a number of local Cultural Algorithms (CAs) and a shared belief space. D-HMP-CA benefits from its dynamic decomposition techniques including the bottom-up and top-down strategies. These techniques divide the problem dimensions into a number of groups which will be assigned to different local CAs. The goal of this article is to evaluate the algorithm scalability. In order to do so, D-HMP-CA is applied on a benchmark of large scale global optimization problems. The results show that the top-down strategy outperforms the bottom-up technique by offering better solutions, while within lower size optimization problems the bottom-up approach presents a better performance. Generally, this evaluation reveals that D-HMP-CA is an efficient method for high dimensional optimization problems due to its computational complexity for both CPU time and memory usage. Furthermore, it is an effective method such that it offers competitive solutions compared to the state-of-the-art methods.

Paper Citation

in Harvard Style

Raeesi N. M. and Kobti Z. (2014). Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 184-191. DOI: 10.5220/0005068801840191

in Bibtex Style

@conference{ecta14,
author={Mohammad R. Raeesi N. and Ziad Kobti},
title={Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005068801840191},
isbn={978-989-758-052-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization
SN - 978-989-758-052-9
AU - Raeesi N. M.
AU - Kobti Z.
PY - 2014
SP - 184
EP - 191
DO - 10.5220/0005068801840191