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1·Standard particle swarm algorithm is easy to fall into local optimum.

标准粒子群算法易陷入局部最优值。

2·BP neural network, as its nature of gradient descent method, is easy to fall into local optimum.

但BP神经网络本质是梯度下降法，容易陷入局部最优。

3·Most swarm intelligence algorithms fall into local optimum easily, and convergence speed is very slow.

大多数群体智能算法容易陷入局部最优，且收敛速度较慢。

4·That they are easy to fall into a local optimum is the shortcoming of conventional optimization methods.

传统的优化方法，即所谓的确定性优化方法的突出缺陷是容易陷入局部最优解。

5·This method is fast while avoiding the shortcoming that the G-S result is easy to trap in local optimum.

该方法不仅具有追迹法计算简单、运算量小的优点，而且克服了G - S算法容易陷入局部最优的缺点。

6·However, the standard Particle Swarm Optimization is easy to fall into local optimum, and slow convergence.

然而，标准粒子群算法存在容易陷入局部最优，后期收敛过慢等问题。

7·Otherwise, the hybrid algorithm can avoid trapping in local optimum and does not need initial feasible solution.

另外，该算法可有效避免陷入局部最优，也不要求提供初始可行解。

8·The experimental result indicates that the modified PSO increases the ability to break away from the local optimum.

实验结果表明，改进后的粒子群算法防止陷入局部最优的能力有了明显的增强。

9·The approaches based on ANN or gradient hill climb algorithm have limitations such as the function form and local optimum.

采用人工神经网或梯度爬山算法均存在对优化函数形式有限制及陷入局部最优等局限性。

10·Local optimum of the permutation-based chromosomes is defined, and a hill-climbing algorithm is proposed to get the local optimum.

定义了基于排列的染色体的局部极值，并以此为基础构造了求极值的爬山算法。