MBSCSO: Multi-strategy Boosted Sand Cat Swarm Optimization for Engineering Applications

The present study introduces MBSCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), with the aim of addressing inherent limitations in the original SCSO algorithm. In MBSCSO, the initial implementation of the enhanced Circle chaotic mapping combined with dynamic opposition-based learning (ECM-OBL) strategy, aims to enhance the global exploration capability of algorithms in the initial stage. Additionally, a nonlinear sensitivity factor (NSF) is adopted to improve the balance between exploration and exploitation. The adaptive alert strategy (AAS), Levy flight strategy (LFS), adaptive Sinh-Cosh spiral attack strategy (ASCAS) and dynamic random search technique (DRST) are designed to enhance the overall performance and efficiency of the algorithm. Finally, the superiority of the presented MBSCSO is validated through comprehensive evaluations on 64 benchmark functions and five typical engineering problems, which clearly demonstrates that the MBSCSO algorithm consistently outperforms or achieves comparable performance to other state-of-the-art optimization approaches.