A new approach in the field of optimization, by Hybridization of Polar Lights Optimizer and Particle Swarm Optimization Algorithm too improve the performance for solving real-world complex applications. The Polar Light Optimizer Algorithm inspired by the behaviour of light in polar coordinates, is combines with The Particle Swarm Optimization Algorithm, which mimics the social behaviour of particles to explore the search space. In this paper the Proposed Hybrid approach seeks to improve convergence rate and solution accuracy, making it more efficient for handling high-dimensional, nonlinear and multimodal optimization problems. The Algorithms performance is assessed with 23(twenty-three) benchmark test functions, which shows the resilience and effectiveness in locating the best and optimal solutions.