针对传统遗传算法(GA)和粒子群算法(PSO)在优化计算过程中存在的优缺点,研究了二者结合的混合算法(GA-PSO),用其优化基于Hermite多项式的投影寻踪回归模型,解决了模型中最佳投影方向的优化计算问题,并运用该模型进行需水量预测.
基于148个网页-相关网页
Results From the description of the solving to the genetic operators, PSO operators and the construction of GA-PSO frame, a integrated GA-PSO programming is presented.
结果从解的描述、遗传算子、PS O运算符的构造再到GA - P SO算法框架，提出了完整的GA - P SO混合规划算法。
Building up decision tree by improved PSO, the paper gives the example to validate that the improved algorithm is better than the original decision tree method and by improved by GA.
将改进的P SO引入到决策树建树方法中，并与传统的决策树方法及使用遗传算法改进后的树进行比较，验证了其优越性。
It selects the optimaler number as a global optimum at every circulation, which makes its result be better than both PSO and GA, then improves the overall performance of the algorithm.
与其他混合最优化算法不同的是，该算法没有破坏粒子群和遗传算法的独立性，而是仅通过全局最优样本把两个算法结合在一起。
应用推荐