一种全程控制的模糊遗传算法在结构优化中的应用
Structure Optimization Based on Global Control Fuzzy Genetic Algorithms
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摘要: 将一种全程控制的模糊遗传算法(FGA算法)引入结构优化设计。区别于一般的标准遗传算法(SGA算法),该算法基于模糊推断机理,可对遗传算法的选择、交叉、变异以及搜索空间的变化进行全程控制。通过2个典型数值多峰函数对FGA算法与SGA算法性能进行了考核和对比,证明该算法在跳出局部最优和搜索效率等方面均有较大改进。将此改进的模糊遗传算法(FGA算法)应用于含整型和离散变量的铆钉连接结构连接效率优化。结果表明:连接效率和优化效率均得到改善。Abstract: A global control Fuzzy Genetic Algorithm (FGA)was applied to the structure optimization.The method given here differs from the standard GA methods (SGA) in taking the whole process of Genetic Algorithm selection,crossover,mutation and the change of searching space into global fuzzy control.The quality of FGA method is verified using two typical mathematical multi-peak functions.The results of simulation show that this kind of fuzzy genetic algorithms can efficiently avoid being entrapped into local optimum and obtain a higher efficiency of optimizing process.A rivet structure,which contains integer and discrete variables,was optimized with FGA method.It shows that a better connecting efficiency with less converging steps can be obtained.
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