提出了三种改进的微粒群算法。
Three improved particle swarm optimization algorithms are proposed.
本文提出了一种新的求解约束优化问题的微粒群算法。
The HPSO combines the particle swarm optimization with constraint optimization and direct search.
该算法通过将整个搜索空间分割成若干子空间，在这些子空间上利用嵌入零搜索算子的微粒群算法进行优化。
The algorithm divides the whole search space into a number of sub-spaces, which are optimized by utilizing the PSO embedded zero search operators.
本文将改进的微粒群算法应用于人脸检测的预处理中，对皮肤概率灰度图像进行阈值化处理，依据肤色分割人脸图像，得到基于肤色的二值图像。
Gray image about skin probability is processed by threshold. According to the color of skin, image containing faces is segmented, and the binary image can be got based on skin-color.
实验结果表明，该并行算法的性能比标准微粒群算法有了很大的提高。
The experimental results show that the performance of the proposed parallel algorithm is better than that of the standard PSO.
提出了基于微粒群和模拟退火算法的图像复原算法。
A method of image restoration based on PSO and simulated annealing algorithm is proposed.
本文讨论了群集智能的两种算法，蚁群智能与微粒群智能。
In this paper, we review ant colony algorithm and particle swarm optimization.
为了保证获得最优水轮机PID调节器参数，本文研究了利用微粒群优化（PSO）算法进行参数优化设计的新方法。
To gain optimization parameters of hydro turbine PID governor, this paper interprets the approach of optimization designing that uses the Particle Swarm Optimization (PSO) algorithm.
微粒群优化算法是求解连续函数极值的一个有效方法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
提出了一种基于微粒群优化(PSO)算法的连续属性离散化方法，很好的解决了建模过程中连续属性的离散化问题。
An algorithm for discretization based on Particle swarm optimization (PSO) is presented, which can settle the problem of continuous attributes discretization in systema modeling perfectly.
针对一类不可微优化问题，本文提出了一个新的算法—极大熵微粒群混合算法。
To solve a class of non-differentiable optimization problems, this paper proposed a new method called maximum-entropy particles swarm optimization algorithm.
提出了一种适用于高维数值优化问题的空间分割微粒群算法。
Space division particle swarm optimization is proposed to solve the high-dimensional numerical optimization problem in this paper.
提出一种具有逻辑时序特征的微粒群优化算法，并将其应用于半导体封装生产线的工序参数优化中。
A kind of particle swarm optimization method with the characteristic of logical time-sequenced is proposed and applied to procedure parameters optimization of semiconductor assembly product line.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
以上述模型为基础，以毁伤概率为优化目标，采用微粒群算法（PSO）对该系统待优化的各参量进行综合优化。
To optimize the damage probability, the particle swarm optimization algorithm (PSO) is adopted to optimize the system parameters, based on above models.
实验结果表明：若选择合适的通讯周期时，该并行微粒群算法不仅具有理想的加速比，而且有效地提高解的质量。
The experiment results show that if the period of communication is selected appropriate, this parallel PSO not only has perfect speedup, but also improves the quality of result.
目前，群智能理论研究领域有两种主要的算法：微粒群优化算法和蚁群优化算法。
The basic and typical algorithm of swarm intelligence is particle swarm optimization and Ant colony oprimation.
该算法能克服基本微粒群优化算法精度较低，易发散的缺点，有较高的搜索效率。
This algorithm can solve the problem of low precision and divergence of basic particle swarm optimization algorithm, so it has higher efficiency of search.
针对协同微粒群优化存在的停滞现象，提出了一种新的基于粒子空间扩展的协同微粒群优化算法。
Aiming at the stagnation exists in the cooperative particle swarm optimization, presents a new kind of the cooperative particle swarm optimization algorithm based on particles spatial extension.
在静态的微粒群多元最优信息规划模式的基础上，提出了微粒群多元最优信息的模糊自适应规划算法。
Fuzzy adaptive programming algorithm based on particle swarm multi-optimum is proposed on the basis of the static particle swarm multi-optimum information programming mode.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
在离散微粒群算法的基础上，提出了一种基于二进制的随机多目标P SO算法，并对感知模型进行覆盖优化。
Then a binary awareness model based on stochastic sensor placement and a stochastic multi-objective PSO arithmetic based on binary system which is applied on awareness model have been present.
微粒群算法是一种新型的进化计算方法，已在许多领域得到了广泛的应用。
Particle swarm optimizer (PSO) is a new evolutionary computation method, which has been successfully applied to many fields.
在分析基本微粒群优化算法的基础上，引进分群思想，提出了一种动态分群的微粒群优化算法(DPSO)。
On the basis of analyzing the particle swarm optimization and introducing the idea of sub-swarms, a particle swarm optimization algorithm with dynamic sub-swarms (DPSO) is proposed.
目前微粒群算法已广泛应用于函数优化、神经网络训练、数据挖掘、模糊系统控制以及其他的应用领域。
Recently, Particle Swarm optimization is applied into function optimization, Neural Networks, data mining, Fuzzy Control System and other application field.
微粒群优化算法（PSO）是目前备受关注的群集智能算法的代表性方法，也是本文研究工作的算法基础。
As a representative swarm-intelligence based optimization algorithm, Particle SwarmOptimization (PSO) algorithm is applied to capacitor optimization in the dissertation.
最后，通过对基于行为学和微粒群算法的路径规划方法的MATLAB仿真，验证了本文算法的有效性。
At last, validate the effectiveness of the algorithm by simulation of the Path Planning algorithm base on Behavior and Particle Swarm Optimization in MATLAB.
为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
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