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Computer Science > Neural and Evolutionary Computing

arXiv:1702.05308 (cs)
[Submitted on 17 Feb 2017 (v1), last revised 12 Sep 2017 (this version, v2)]

Title:Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach

Authors:Shubham Dokania, Ayush Chopra, Feroz Ahmad, Anil Singh Parihar
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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.
Comments: 8 pages, accepted at IJCCI 2017
Subjects: Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1702.05308 [cs.NE]
  (or arXiv:1702.05308v2 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.1702.05308

Submission history

From: Shubham Dokania [view email]
[v1] Fri, 17 Feb 2017 11:47:50 UTC (2,585 KB)
[v2] Tue, 12 Sep 2017 11:46:59 UTC (893 KB)
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Shubham Dokania
Ayush Chopra
Feroz Ahmad
Om Prakash Verma
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