Multi-objective optimization of air bearings using hypercube-dividing method

Nenzi Wang*, Kuo Chiang Cha

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

23 Scopus citations


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 HDM can also be confined to some useful resolution to improve its global search capability.

Original languageEnglish
Pages (from-to)1631-1638
Number of pages8
JournalTribology International
Issue number9
StatePublished - 09 2010


  • Air bearing
  • Hypercube-dividing method
  • Multi-objective optimization
  • Pareto optimality


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