A Particle Swarm Optimization with Random Particles and Fine-Tuning Mechanism for Nonconvex Economic Dispatch

This study presents a new approach to the economic dispatch problems with valve-point effects. The practical economic dispatch problem has a nonconvex cost function with equality and inequality constraints that it is difficult to find the optimal solutions using any mathematical approaches. A Particle Swarm Optimization (PSO) with Random Particles and Fine-tuning mechanism (PSO-RPFT) is proposed to solve economic dispatch problem. The proposed developed in such a way that PSO with Constriction Factor (PSO-CF) is applied as a based level search which can give a good direction to the optimal global region. Random particles and fine-tuning mechanism is used as a fine tuning to determine the optimal solutions at the final. Effectiveness of the proposed method is demonstrated on 3 example systems and compared to that of SA, GA, EP. Results show that the proposed method is more effective in solving economic dispatch problem.