Multiobjective particle swarm optimization based on differential evolution for environmental/economic dispatch problem

This paper presents a multiobjective particle swarm optimization based on differential evolution (IMOPSO-DE) algorithm for environmental/economic dispatch (EED) problem. The algorithm adopted differential evolution to increase the diversity of the Pareto set. Circular crowded sorting approach helped to generate a set of well-distributed Pareto-optimal solutions in one run. The global best individuals in multiobjective optimization domain are redefined through a new multiobjective fitness roulette technique. Several optimization runs of the proposed approach have been carried out on the IEEE30-BUS six-generator test system. Simulation results revealed that proposed approach obtained high-quality solutions and was able to provide a satisfactory compromise solution in almost all the trials, thereby validating the efficacy and applicability of the proposed approach over the real-word multiobjective optimization problems.