The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps

In the present study, multi-objective optimization of centrifugal pumps is performed in three steps. In the first step, efficiency (eta) and the required net positive suction head (NPSHr) in a set of centrifugal pumps are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained in the second step for modeling of eta and NPSHr with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, a multi-objective particle swarm optimization method (MOPSO) is used for Pareto-based optimization of centrifugal pumps considering two conflicting objectives, eta and NPSHr. The Pareto results of the MOPSO method are also compared with those of a multi-objective genetic algorithm (NSGA II). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of centrifugal pumps can be discovered by Pareto-based multi-objective optimization of the obtained polynomial metamodels representing eta and NPSHr characteristics.