Elitist Multi-objective Particle Swarm Optimization with Fuzzy Multi-attribute Decision Making for Power Dispatch

Abstract

Elitist multi-objective particle swarm optimization is proposed for solving multi-objective power dispatch. The multi-objective particle swarm optimization utilizes fuzzy multi-attribute decision making, including maximizing the diversity of Pareto-optimal solutions, limiting the number of Pareto-optimal solutions to a manageable size as well as extracting the best compromise solution. The simulation results of several optimization runs indicate that the multi-objective particle swarm optimization yields a better distributed Pareto fronts and wider extension range than random particle swarm optimization, fitness sharing-cum-niching particle swarm optimization, and strength Pareto dominance-based particle swarm optimization in a faster computing manner. Moreover, the best compromise solution obtained has a good trade-off characteristic among all objectives.