A new multi-objective particle swarm optimization for economic environmental dispatch

This paper investigates a new approach of computation using particle swarm in order to resolve economic environmental dispatch problem. This approach is called accelerated multiobjective particle swarm optimization (AMOPSO) which incorporates vector function as objective function and uses matrix computation and updates solutions set, in each iteration, for developing the Pareto front unlike the existing multi-objective algorithms which use an external archive. We apply this approach to resolve the problem which treats fuel cost, NOx emissions and active power losses as competing objectives.