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Open Access

ARTICLE

Enhanced Sand Cat with Selective Opposition (ESCSO) Algorithm for Optimization and Engineering Problems

Aisha Tanveer1, Noraini Ibrahim1, Muhammad Zubair Rehman2,*, Abdullah Khan3, Nazri Mohd. Nawi1
1 Soft Computing & Data Mining Centre (SMC), Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia
2 Faculty of Computing & IT, Sohar University, Sohar, Oman
3 Institute of Computer Science and Information Technology, The University of Agriculture Peshawar, Peshawar, Pakistan
* Corresponding Author: Muhammad Zubair Rehman. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085167

Received 06 May 2026; Accepted 11 June 2026; Published online 05 August 2026

Abstract

Metaheuristic optimization algorithms have gained wide adoption in engineering and scientific domains. However, many swarm-based methods struggle to balance exploration and exploitation, often converging prematurely on suboptimal solutions. The Sand Cat Swarm Optimization (SCSO) algorithm is one such method, with limited exploration ability constraining its performance on complex problem landscapes. This paper introduced the Enhanced Sand Cat with Selective Opposition (ESCSO) algorithm which combines opposition-based learning with a velocity mechanism to overcome this limitation. In ESCSO, under-performing candidates referred to as Sigma-variant cats are identified using Spearman correlation and replaced with their opposite solutions to inject diversity into the search process. Stronger candidates termed Sigma cats, act as elite guides pulling the search toward better regions. A PSO-inspired velocity update governs both roles, keeping exploration and exploitation in balance rather than letting one dominate. Tested across 30 benchmark functions plus two real engineering problems, reflectarray antenna design and microgrid energy management, ESCSO achieves competitive convergence, solution quality, and robustness when compared to recent state-of-the-art methods.

Keywords

Opposition-based learning; metaheuristics; search algorithm; sand cat optimization; swarm intelligence; microgrid energy management; Reflectarray antenna

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