Optimizing Benchmark Functions using Particle Swarm Optimization PSO

Optimization is a very important step in many automated systems in different sectors because minimizing the search space to find the best solution and hence minimizing the time required by any automated system. This paper implements and evaluates Particle Swarm Optimization (PSO) on four benchmark optimization functions: Rastrigin, Sphere, Rosenbrock, and Ackley by selecting and tuning parameter and enhancing algorithm performance in several optimization circumstances. The PSO algorithm's performance is assessed based on the best solution, computational efficiency, and runtime to enhance theoretical knowledge of how the PSO algorithm interacts with mathematical landscapes by applying it to diverse scalar functions. The analyzing process uncovered the points of strength and weaknesses of the PSO algorithm to enhance the diverse applications. By comparing a specific swarm method and its applications with collection of functions, the study advances the mathematical understanding of the algorithm. The outcomes demonstrate that the PSO algorithm can effectively navigate complex search spaces and find optimal solutions for various optimization problems and the obtained result was fairly good by achieving fast speed up to 0.123 second.