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Computer Science > Neural and Evolutionary Computing

arXiv:2108.12610 (cs)
[Submitted on 28 Aug 2021]

Title:Chaos embedded opposition based learning for gravitational search algorithm

Authors:Susheel Kumar Joshi
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Abstract:Due to its robust search mechanism, Gravitational search algorithm (GSA) has achieved lots of popularity from different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.
Comments: 33 pages, 5 Figures
Subjects: Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
Cite as: arXiv:2108.12610 [cs.NE]
  (or arXiv:2108.12610v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2108.12610
Journal reference: Applied Intelligence, 2022,1-20
Related DOI: https://doi.org/10.1007/s10489-022-03786-9

Submission history

From: Susheel Joshi PhD [view email]
[v1] Sat, 28 Aug 2021 09:22:39 UTC (1,672 KB)
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