To improve the performance of optimization algorithms in complex function optimization scenarios, this paper proposes an improved version of the dhole optimization algorithm, named the Fuch-experience exchange-tangent flight dhole optimization algorithm (FETDOA). Based on the original dhole optimization algorithm (DOA), the FETDOA adopts Fuch chaotic mapping to enhance the uniformity of the initial population and expand the search coverage. The tangent flight strategy introduces controllable perturbations in the orthogonal subspace of the prey attraction direction to ensure convergence and strengthen local exploration, thus alleviating premature convergence and local stagnation in complex multimodal problems. The improved experience exchange strategy (EES) enhances information sharing among individuals and improves the efficiency of collaborative search, ultimately achieving fast and high-precision convergence. To verify its performance, the FETDOA is compared with eight state-of-the-art algorithms, and the Wilcoxon rank-sum test is used for statistical significance analysis. Experimental results show that the FETDOA is significantly superior to the comparison algorithms in terms of mean and standard deviation on most test functions. Finally, three engineering cases are adopted to demonstrate the excellent performance of the FETDOA in engineering applications. In summary, the FETDOA has stronger convergence accuracy and stability in complex function optimization, providing an efficient solution for practical problems such as engineering optimization and parameter tuning.