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首页> 外文期刊>AIAA Journal >Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations
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Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations

机译:基于单独约束近似的混合整数非线性规划问题的遗传算法

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摘要

A new approach is described for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables and multivariate approximation of the individual functions' responses in terms of several continuous design variables. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.
机译:描述了一种新方法,用于减少遗传算法(GA)对具有连续和离散混合设计变量的优化问题所要求的适应度和约束函数评估数量。 GA的拟议增加功能使搜索更加有效,并且一代又一代地迅速提高了适用性。增加的内容涉及作为离散和连续设计变量的函数的存储器,以及根据几个连续设计变量对各个函数的响应进行多元近似。该近似值表明了具有格栅加劲肋的复合圆柱壳的最小重量设计。

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