DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance

Oluwatayomi Rereloluwa Adegboye; Afi Kekeli Feda; Oluwaseun Racheal Ojekemi; Ephraim Bonah Agyekum; Baseem Khan; Salah Kamel

doi:10.1038/s41598-023-50910-x

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance

Sci Rep. 2024 Jan 17;14(1):1491. doi: 10.1038/s41598-023-50910-x.

Authors

Oluwatayomi Rereloluwa Adegboye¹, Afi Kekeli Feda², Oluwaseun Racheal Ojekemi³, Ephraim Bonah Agyekum⁴, Baseem Khan⁵, Salah Kamel⁶

Affiliations

¹ Management Information System Department, University of Mediterranean Karpasia, Mersin-10, Turkey.
² Management Information System Department, European University of Lefke, Mersin-10, Turkey.
³ Business Administration Department, European University of Lefke, Mersin-10, Turkey.
⁴ Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Ekaterinburg, Russia, 620002.
⁵ Department of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia. baseem.khan04@gmail.com.
⁶ Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt.

Abstract

This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.