Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies
Lee, D-S., Gonzalez, L. F., Periuax, J., & Bugeda, G. (2009) Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies. In Negnevistsky, M. (Ed.) Proceedings of the 3rd International Workshop on Artificial Intelligence in Science and Technology (AISAT 2009), The University of Tasmania, University of Tasmania, Hobart, Tasmania, pp. 1-10.
One of the main aims in artificial intelligent system is to develop robust and efficient optimisation methods for Multi-Objective (MO) and Multidisciplinary Design (MDO) design problems. The paper investigates two different optimisation techniques for multi-objective design optimisation problems. The first optimisation method is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The second method combines the concepts of Nash-equilibrium and Pareto optimality with Multi-Objective Evolutionary Algorithms (MOEAs) which is denoted as Hybrid-Game. Numerical results from the two approaches are compared in terms of the quality of model and computational expense. The benefit of using the distributed hybrid game methodology for multi-objective design problems is demonstrated.
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|Item Type:||Conference Paper|
|Keywords:||Hybid Games, Optimisation, MDO, Artificial Intelligence|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Flight Dynamics (090106)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2009 Artificial Intelligence in Science and Technology|
|Deposited On:||12 Jul 2010 00:51|
|Last Modified:||10 Aug 2011 16:49|
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