标准粒子群算法易陷入局部最优值。
Standard particle swarm algorithm is easy to fall into local optimum.
改进后的遗传算法通过二次演化能够有效地避免算法中容易陷入局部最优值的缺陷。
Improved Genetic Algorithms can avoid the defect of reaching the part best value easily by the twice evolution.
实验结果证明，优化后的BP网络可有效地避免收敛于局部最优值，大大地缩短了训练时间。
The results show the optimized BP neural network can effectively avoid converging on local optimum and reduce training time greatly.
算法中采用的记忆指导搜索策略重点搜索了各记忆段的局部最优值，避免了全局搜索的盲目性；
The adoption of remembrance-guided search method emphasizes local optimum value in each remembrance segment, which avoids the blindness of global search.
迭代tfidf算法属于爬山算法，初始值的选取对精度影响较大，算法容易收敛到局部最优值。
Iterative TFIDF algorithm belongs to hill-climbing algorithm, it has the common problem of converging to local optimal value and sensitive to initial point.
传统K均值算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动，容易陷入局部最优值。
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.
且改进的粒子群算法在模糊神经网络权值的训练中收敛速度和跳出局部最优的能力都要比BP算法更优。
And, in FNN weight training, improved PSO in the convergence rate and the ability to jump out to local optimum algorithm is better than BP.
在不影响收敛速度的情况下，它能够很好解决局部最优以及对初始值敏感的问题。
Without prejudice to the speed of convergence, it can resolve the problems of local optimal and sensitivity to initial values.
本文在局部凸空间中对集值映射最优化问题引入超有效解的概念。
In this paper, we introduce a concept of super efficient solution of the optimization problem for a set-valued mapping.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
该算法把惯性权重和学习因子分别通过结合全局和局部最优最小值来进行改写，速度更新公式也做了相应的简化。
The inertia weight and the acceleration coefficient of the algorithm were both adapted by the global best and the local best minimum (GLBM). The velocity equation of the GLBM-PSO was also simplified.
该算法把惯性权重和学习因子分别通过结合全局和局部最优最小值来进行改写，速度更新公式也做了相应的简化。
The inertia weight and the acceleration coefficient of the algorithm were both adapted by the global best and the local best minimum (GLBM). The velocity equation of the GLBM-PSO was also simplified.
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