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Authors: Mohammad R. Raeesi N. and Ziad Kobti

Affiliation: University of Windsor, Canada

Keyword(s): Cultural Algorithm, Multi-Population, Heterogeneous Sub-population, Dynamic Decomposition, Large Scale Global Optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Co-Evolution and Collective Behavior ; Computational Intelligence ; Concurrent Co-Operation ; Evolutionary Computing ; Society and Cultural Aspects of Evolution ; Soft Computing ; Swarm/Collective Intelligence

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. (More)

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Paper citation in several formats:
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 (IJCCI 2014) - ECTA; ISBN 978-989-758-052-9, SciTePress, pages 184-191. DOI: 10.5220/0005068801840191

@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 (IJCCI 2014) - ECTA},
year={2014},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005068801840191},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2014) - ECTA
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
PB - SciTePress