文章基本信息

标题：An Approach to Optimize ANN Meta Model with Multi Objective Genetic Algorithm for Multi-Disciplinary Shape Optimization
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作者：Ram Krishna Rathore ; Amit Sarda ; Rituraj Chandrakar 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：1
页码：200-207
出版社：International Journal of Soft Computing & Engineering
摘要：In several design cases, designers need to optimize a number of responses concurrently. A general approach for the multiple response cases optimization start with using the regression models to calculate the correlations between response functions and control factors. Then, a system for collecting various response functions together into a one quantity, such as an objective function, is engaged and, at last, an optimization technique is used to calculate the best combinations for the control functions. A different method proposed in this paper is to use an artificial neural network (ANN) to calculate the parameter response functions. At the optimization stage, a multi objective genetic algorithm (MOGA) is used in combination with an objective functions to establish the optimum conditions for the control functions. A crane hook example has been taken to optimize multiple shape parameter responses to with stand a new loading condition. The results estimate the reduction in mass and sufficient factor of safety to show the proposed approach for the optimization of multi- disciplinary shape optimization problems.
关键词：ANN; MOGA; Shape optimization; Meta;modeling