Abstract: A moth-flame optimization algorithm with adaptive dynamic disturbance coefficient and piecewise adjustable search strategy (ADMFO) is proposed to solve a large-scale complex optimization problem. The adaptive dynamic perturbation coefficient strategy is adopted to improve the global searching ability of the algorithm and avoid the algorithm falling into the local optimum. The segmented search strategy can balance the proportion of global exploration and local development, so as to achieve a better search strategy. 15 unimodal and multi-peak complex high-dimensional function optimization experiments are conducted, comparing particle swarm algorithm, sine cosine algorithm, butterfly optimization algorithm, the gray wolf algorithm, and the four improved algorithms proposed in other literature. The experimental data proves that the improved algorithm has better optimization accuracy and stability.