Impact of Structural Bias on the Sine Cosine Algorithm: A Theoretical Investigation Using the Signature Test

Rajwar, K and Deep, K and Mathirajan, M (2024) Impact of Structural Bias on the Sine Cosine Algorithm: A Theoretical Investigation Using the Signature Test. In: 9th International Conference on Metaheuristics and Nature Inspired Computing, META 2023, 1 November 2023through 4 November 2023, Marrakech, pp. 131-141.

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Abstract

Metaheuristic algorithms have been recognized for their effectiveness in solving non-convex and non-linear complex optimization problems. These algorithms are influenced by landscape bias, guided by objective function values, and algorithmic operator bias directed by the operator used in the algorithms. The presence of algorithmic operator bias, also known as structural bias, forces the population to revisit a particular region, badly affecting the algorithm�s exploration capacity. Also, since the population revisits the same place without gaining new information, it increases computational costs and slows the convergence rate. Therefore, it is crucial to identify and address structural bias to enhance algorithm performance and reduce computational time. To the best of our knowledge, no previous study has focused on investigating the structural bias of the Sine Cosine Algorithm (SCA) in the existing literature. Therefore, the main objective of this study is to examine the structural bias present in the SCA, a widely used metaheuristic algorithm. To investigate structural bias signature test is employed. Additionally, average Euclidean distances of the population is calculated to assess spatial relationships and overall distribution. Our analysis uncovers a prominent bias in the SCA towards the axes and the origin, suggesting a strong tendency to converge towards specific regions within the search space. By understanding and characterizing this bias, we provide valuable insights into the behavior of the SCA, which can contribute to the research community�s understanding and guide future improvements in algorithm design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Item Type:	Conference Paper
Publication:	Communications in Computer and Information Science
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords:	Convex optimization, Algorithmics; Complex optimization problems; Linear complexes; Meta-heuristics algorithms; Non linear; Signature tests; Sine-cosine algorithm; Structural bias; Theoretical analyze; Theoretical investigations, Heuristic algorithms
Department/Centre:	Division of Interdisciplinary Sciences > Management Studies
Date Deposited:	14 Oct 2024 10:15
Last Modified:	14 Oct 2024 10:15
URI:	http://eprints.iisc.ac.in/id/eprint/86569

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