Optimization algorithms are essential for addressing intricate real-world challenges by effectively identifying optimal or near-optimal solutions. The Ant-lion Optimization Algorithm (ALO) has proven to be effective in various optimization scenarios; however, its performance can often be enhanced through hybridization with other meta-heuristic methods. This research examines the effects of integrating Particle Swarm Optimization (PSO) with the ALO algorithm by contrasting the results obtained from the original ALO with those from the hybridized approach. The efficacy of the hybrid method was assessed using a range of benchmark functions (F1 to F23), which encompass various optimization problems, including uni-modal, multi-modal, and fixed-point functions. The findings indicate that the PSO-ALO hybridization significantly boosts optimization performance for several functions, such as F1, F3, F6, F7, and F9, where the function values showed a notable decrease, reflecting improved solution accuracy and convergence rates.