Multi-objective Optimization of Air Bearings Using Hypercube-dividing Method


The commonly used genetic algorithm (GA) in solving a multi-objective optimization problem (MOOP) is replaced by the hypercube-dividing method (HDM) in this air bearing optimization study. In the new method the dividing of hypercubes in the design space is conducted based on the size and Pareto rank of hypercube. A comparison of the HDM- and GA-based method for the MOOP is performed. The results show that the solution obtained by the HDM is improved with more selections and less computing load. The search in the HUM can also be confined to some useful resolution to improve its global search capability.