Multi strategy improvement of sand cat swarm optimization algorithm and its application

In the field of intelligent manufacturing systems and automation, optimization algorithms play a crucial role in enhancing system performance and solving complex engineering problems. The Sand Cat Swarm Optimization (SCSO) algorithm, as an emerging swarm intelligence optimization method, shows potential in addressing complex optimization issues but has limitations in global search capability and avoiding local optima. To address these shortcomings, this paper proposes an improved Sand Cat Swarm Optimization (ISCSO) that integrates multiple strategies.. The algorithm integrates a spiral shrinking walk strategy to enhance global search capability, enabling individuals to better focus on the optimal solution. Additionally, a triangular walking strategy is introduced to improve local search capability. Moreover, a lens opposition-based learning strategy is employed to dynamically adjust individual positions, broadening and deepening the search, which accelerates the convergence speed. The ISCSO algorithm is compared with seven other algorithms using 12 benchmark functions and applied to practical engineering problems. Experimental results demonstrate that ISCSO outperforms other algorithms in terms of convergence speed and optimization ability, achieving more accurate solutions in a shorter time. This validates its effectiveness in optimizing production scheduling and improving system performance, with broad application potential.