Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach

Shubham Dokania, Ayush Chopra, Feroz Ahmad, Anil Singh Parihar

Abstract

Operational maturity of biological control systems have fuelled the inspiration for a large number of mathematical and logical models for control, automation and optimisation. The human brain represents the most sophisticated control architecture known to us and is a central motivation for several research attempts across various domains. In the present work, we introduce an algorithm for mathematical optimisation that derives its intuition from the hierarchical and distributed operations of the human motor system. The system comprises global leaders, local leaders and an effector population that adapt dynamically to attain global optimisation via a feedback mechanism coupled with the structural hierarchy. The hierarchical system operation is distributed into local control for movement and global controllers that facilitate gross motion and decision making. We present our algorithm as a variant of the classical Differential Evolution algorithm, introducing a hierarchical crossover operation. The discussed approach is tested exhaustively on standard test functions as well as the CEC 2017 benchmark. Our algorithm significantly outperforms various standard algorithms as well as their popular variants as discussed in the results.

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Paper Citation


in Harvard Style

Dokania S., Chopra A., Ahmad F. and Parihar A. (2017). Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 207-214. DOI: 10.5220/0006490302070214


in Bibtex Style

@conference{ijcci17,
author={Shubham Dokania and Ayush Chopra and Feroz Ahmad and Anil Singh Parihar},
title={Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006490302070214},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach
SN - 978-989-758-274-5
AU - Dokania S.
AU - Chopra A.
AU - Ahmad F.
AU - Parihar A.
PY - 2017
SP - 207
EP - 214
DO - 10.5220/0006490302070214