Multi-Objective Optimization of Centrifugal Pumps Using Particle Swarm Optimization Method

In the present study, multi-objective optimization of centrifugal pumps is performed at three steps. At the first step, η and NPSHr in a set of centrifugal pump are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of η and NPSHr with respect to geometrical design variables. Finally, using obtained polynomial neural networks, Multi-Objective Particle Swarm Optimization method (MOPSO) are used for Pareto based optimization of centrifugal pumps considering two conflicting objectives, η and NPSHr. The Pareto results of PSO method are also compared with that of 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 meta-models representing their η and NPSHr characteristics.