Multi-strategy improved sparrow search algorithm

Addressing issues such as the decrease in population diversity and the tendency to fall into local optima when the sparrow search algorithm (SSA) approaches the global optimal solution, this paper proposes a multi-strategy improved sparrow search algorithm (MISSA). Firstly, the Halton sequence is introduced to enrich the diversity of the initial population, thereby enhancing the algorithm's traversal capabilities in optimization. Secondly, the sine cosine algorithm (SCA) is integrated into the position updating mechanism of explorers, and a nonlinear dynamic learning factor is introduced to balance the local and global search capabilities, accelerating convergence speed. Finally, the Lévy flight strategy is employed in the position updating mechanism of followers to perturb and mutate the current optimal solution, strengthening the algorithm's ability to escape from local optima.The effectiveness of the improved strategy is validated through experiments on 14 benchmark functions, and the results show that MISSA exhibits higher solution accuracy and faster convergence speed. Additionally, in the welding beam optimization problem, MISSA achieves a lower total objective function value and a reduced standard deviation, further verifying its superiority and applicability in handling practical engineering optimization problems.