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

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.