A Comparison Study of PSO with Different Update Equations in Solving Economic Dispatch Problem

A number of particle swarm optimization (PSO) algorithms have been proposed for solving power economic dispatch (ED) problems. The crucial differences among these PSO are the updating equations they utilized. In this paper, we conduct a comparison study of four PSO with different updating equations, i.e., inertia weight PSO (WPSO), bare-bone PSO (BBPSO), quantum-behaved PSO (QPSO) and biogeography-based learning PSO (BLPSO), for solving ED problems with various physical constraints. Based on the simulation results and analysis, some useful insights are concluded, which may provide some guidelines on the choice of PSO for solving the ED problems.