Title: A sine-cosine algorithm blended grey wolf optimisation algorithm for partitional clustering

Authors: Gyanaranjan Shial; Chita Ranjan Tripathy; Sabita Sahoo; Sibarama Panigrahi

Addresses: Department of Computer Science and Engineering, Sambalpur Univeristy, Sambalpur, Odisha, India ' Department of Computer Science and Engineering, Biju Patnaik University of Technology, Bhubaneswar, Odisha, India ' Department of Mathematics, Sambalpur University, Sambalpur, Odisha, India ' Department of Computer Science Engineering and Application, Sambalpur University, Sambalpur, Odisha, India

Abstract: Over the last few decades, partitional clustering algorithms have been emerged as one of the most promising clustering algorithms that find groups among data items. Motivated from this, we have proposed a hybrid sine-cosine algorithm (SCA) blended grey wolf optimisation (GWO) algorithm for partitional data clustering. This algorithm selects near-optimal cluster centres using leadership approach of GWO and explorative strategy of SCA. Here, the sine and cosine functions are used to generate more diversified solutions around the mutant wolf of each search agents. Therefore, a tradeoff is maintained between exploration and exploitation which enjoys the benefits from both the algorithms. An extensive simulation work is carried out for clustering 11 benchmark datasets using four performance measures. Additionally, a comparative performance analysis (statistical) is conducted against GWO, PSO, SCA, JAYA and K-means using Duncan's multiple range test and Friedman and Nemenyi hypothesis test. The test confirms the supremacy of our proposed algorithm.

Keywords: grey wolf optimiser; JAYA algorithm; sine-cosine algorithm; SCA; particle swarm optimisation; PSO; partitional clustering; K-means algorithm.

DOI: 10.1504/IJCVR.2025.144779

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.2, pp.198 - 232

Received: 27 Sep 2022
Accepted: 03 Aug 2023
Published online: 03 Mar 2025 *

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