Optimization algorithms play a key role in solving complex computational problems across various disciplines. In this paper, we introduce a new hybrid model that integrates the Dragonfly Algorithm (DA) and the Ant Lion Optimizer (ALO) for optimization performance enhancement. The new hybrid model integrates the exploration ability of DA and the exploitation ability of ALO with the expectation of better convergence and optimal solutions. We applied the model to optimize 23 benchmarking functions, and the results indicate that the hybrid model is better than the traditional algorithm approach in generating smaller errors and scores in the area of optimization. The study indicates that the proposed hybrid model offers a feasible solution to solving high-dimensional optimization problems.