Hybrid Particle Swarm Optimization with Science Cosine Algorithm and Mathematical Equations for Enhancing Robot Path Planning

This paper introduces a hybrid metaheuristic algorithm that combines Particle Swarm Optimization (PSO) algorithm with Sine Cosine algorithm and Mathematical equations. The algorithm makes a contribution to optimization field by providing better strategy for finding the global minimal value, enhancing exploration and exploitation features, speeding up the converge rate over the tested benchmark optimization problems. The results show that combining SCA and ME with PSO in a new hybrid algorithm called PSE. However, the new algorithm overcome the drawbacks of PSO and it effectively solve high dimensional optimization problems. PSE algorithm is being applied in order to enhance robot path planning, robots can find a high efficiency specification objective when powered by hybrid algorithm. The objective of optimization is to reduce the path lengths to target. Autonomous path planning is necessary to prevent obstacles during the motion of robot. The result show that the algorithm proposed is able to give better performance in reaching targets.