遗传优化算法及含有模糊目标和模糊约束的机械优化设计

针对机械工程中的非线性约束优化的工程问题 ,提出了一种新的遗传算法。该方法在遗传算法中通过去掉等式约束、构造浮点型编码向量、精心设计动态遗传及变异算子等改造操作 ,较大地提高了寻优效率和寻优能力 ,并用Matlab语言开发了相应软件。对设计的算法与一般遗传算法、变尺度法以及随机搜索方法进行算例比较。对于含有模糊目标和模糊约束冗余系统可靠性优化设计问题 ,通过定义隶属函数 ,把问题转化为清晰的普通优化问题利用改进的算法求解 ,以提高求解的精度和可靠性

IMPROVED GENETIC OPTIMIZATION ALGORITHM AND ITS APPLICATIONS IN MECHANICAL OPTIMIZATION DESIGN WITH FUZZY OBJECT AND FUZZY CONSTRAINED FUNCTION

A new improved genetic optimization algorithm(IGOA) fit for solving nonlinear constraint problem of mechanical engineering was proposed.Based on genetic algorithm evolution between generations after removing equation constraint,constructing float-type coding vectors and well-connected planning dynamic inherit and aberrance operator and so on,improvement operation was used to speed the rate of convergence and to improve ability of solution.The soft was developed with Matlab language.The contradistinctive analysis was done among randomness searching optimization method and variable scale optimization method and general genetic optimization.Redundancy system reliability optimization design with fuzzy object and fuzzy restriction was transferred into clear ordinary optimization question by defining subjection function and searching the answer with IGOA.The method is successful,simple and moderate by example calculation,and it has high rate of convergence,accuracy and reliability and is effective for the mechanical optimization problem.