离散变量模糊优化的组合形遗传算法及应用

Combined shape genetic algorithm for fuzzy optimization with discrete variables and its application

为解决离散变量结构优化客观追求的应该是"满意解"的问题,提出离散变量模糊优化的模型,构造了离散变量模糊优化的对称解法.把离散组合形算法作为组合形操作算子融合到遗传算法中,构造一种离散变量结构优化算法-组合形遗传算法.在建立的对称模糊优化模型中,利用交模糊判决,将模糊优化问题转化成非模糊优化问题来求解,然后运用组合形遗传算法进行非模糊优化问题的求解.最后通过算例证明该方法具有良好的效果,为工程结构优化设计提供具有参考价值的理论依据.

In order to solve the issue that the objective pursuit of structural optimization with discrete variables should be ＂satisfactory solution＂,the model of fuzzy optimization with discrete variables was established and symmetry solution of fuzzy optimization with discrete variables is constructed. This paper embedded the combined shape algorithm into genetic algorithm as a combined shape operator, and consequently proposed a hybrid genetic algorithm for structural optimization with discrete variables. In the model of symmetrical fuzzy optimization, fuzzy optimization problem is changed into non-fuzzy optimization problem by using intersection fuzzy decision, then combination genetic algorithm is proposed to solve the non-fuzzy optimization problems. Finally, the results of the example prove that the method has good effect and can provide reference for engineering structure optimization design.