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Multidisciplinary and multiobjective optimization: Comparison of several methods

By Benoît Guédas and Jérôme Picard

Abstract

Engineering design of complex systems is a decision making process that aims at choosing from among a set of options that implies an irrevocable allocation of resources. It is inherently a multidisciplinary and multiobjective process. The paper describes some classical multidisciplinary optimization (MDO) methods with their advantages and drawbacks. Some new approaches combining genetic algorithms (MOGA) and collaborative optimization (CO) are presented. They allow to: 1) increase the convergence rate when a design problem can be broken up regarding design variables, and 2) provide an optimal set of design variables in case of multi-level multi-objective design problem

Topics: Multidisciplinary Design Optimization, Bi-Level Collaborative Optimization, Genetic Algorithm, Pareto Frontier
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.413.7042
Provided by: CiteSeerX
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