Modelling preferences in multi-objective engineering design

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• 作者：Javier Sanchis ; Miguel A. Martí ; nez ; Xavier Blasco ; Gilberto Reynoso-Meza
• 关键词：Multi-objective optimization ; Engineering design ; Preferences ; Genetic algorithms ; Physical programming
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2010
• 出版时间：December 2010
• 年：2010
• 卷：23
• 期：8
• 页码：1255-1264
• 全文大小：801 K

文摘

The multi-objective optimization strategy called physical programming (PP) provides engineers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective or specification design, preferences are established through linguistic categories to which numerical values are assigned. In PP, this mapping is made using preference functions as piecewise splines whose curvatures are calculated with an expensive and iterative algorithm. However, mapping between design parameter space and objective space may be largely non-convex and is uninfluenced by the use of gradient-based methods for solving the optimization problem. In this paper, the philosophy of the PP method has been used, but two components have been totally redesigned: a simpler algorithm is used for the construction of preference functions; and the optimizer is replaced by a genetic algorithm that avoids possible local minima problems. Three engineering applications are shown to illustrate the value of this new method.