Generation of Pareto optimal solutions using generalized DEA and PSO

Meta-heuristic methods such as particle swarm optimization and genetic algorithms have been applied in solving multi-objective optimization problems, and have been observed to be useful for generating a good approximation of Pareto optimal solutions. This paper suggests a multi-objective particle swarm optimization (MOPSO) utilizing generalized data envelopment analysis (GDEA) in order to decide adaptively parameters of MOPSO as well as to improve the convergence and the diversity in the search of solutions. In addition, the effectiveness of the proposed method using GDEA will be investigated by comparison with conventional methods through several numerical examples.