The artificial lemming algorithm is a newly proposed metaheuristic method that simulates four distinct behaviors of lemmings to effectively explore complex search spaces. However, it still suffers from premature convergence, limited exploration, lack of robustness, and susceptibility to local optima. To address these limitations, a multi-strategy improved artificial lemming algorithm is proposed. First, the Halton sequence is employed to generate a uniformly distributed initial population, enhancing global search capability. Second, an elite pool strategy combined with inertia weights is introduced to reduce excessive reliance on the best individuals and to improve the population′s ability to jump across the search space, thereby suppressing premature convergence. Finally, a nonlinear weighted golden sine strategy, combined with foraging behavior, is incorporated in the later stages of iteration to enhance the precision and stability of local search. To verify the performance of the improved algorithm, experiments are conducted on the CEC2017 benchmark function set, and statistical analysis is performed using the Wilcoxon rank-sum test. Experimental results show that the improved algorithm outperforms five comparative algorithms in terms of convergence speed, optimization accuracy, and stability. Compared to the original algorithm, the improved version achieves an average error reduction of 27.36% and a reduction of 36.99% in the average standard deviation. In three engineering optimization problems, the improved algorithm obtains the minimum objective function values in all cases, demonstrating better applicability and superiority over the comparative methods.