Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation

The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. first layer involves γ and λ, values which are introduced to describe current condition characteristics searched solution diversifies particles when it is converging too much on some optima. second partitioning tries prevent premature convergence. With two mechanisms, PSPSO presents simpler way making communicate with each other without compromise computational time. proposed algorithm was compared different variants (PSO) using benchmark functions as well IEEE 10-unit unit commitment problem. Results proved effectiveness its competitive advantage in comparison published PSO variants.