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.
论文尝试使用微粒群优化算法与GIS相结合解决超市最优选址问题。
This paper demonstrates that using particle swarm optimization approach to solve optimal location of supermarkets based on GIS.
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.
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.
提出了基于一种改进微粒群优化算法的移动机器人在已知环境信息下的路径规划方法。
A path planning approach based on a modified particle swarm optimization (PSO) is presented for mobile robot in known static environments.
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.
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.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
A PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
提出一种两群替代微粒群优化算法(TSSPSO)，并对算法参数进行分析和对算法方程进行修正。
In this paper, two sub-swarms substituting particle swarm optimization algorithm (TSSPSO) is proposed. The algorithm parameters are analyzed and the iteration equations are amended.
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.
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.
论文研究结果丰富了不确定优化理论，拓宽了微粒群优化算法的应用领域，为PSO在复杂不确定系统中的应用提供了有益的指导。
Research results of this dissertation enrich theory of the uncertain optimization, expand application domain of PSO, and provide beneficial guides for applying PSO in complicated uncertain systems.
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.
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.
实验结果表明该改进微粒群算法可以有效地解决高维数值优化问题。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
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.
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.
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.
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。
The image threshold segmentation algorithm based on the Particle Swarm Optimization (PSO) combined with the rough entropy based on boundary region is presented.
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.
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.
针对这一问题，分析了海军维修保障系统结构，建立了维修保障系统模型，采用微粒群算法对模型进行智能优化。
Aiming at this problem, naval maintenance system structure is analyzed, maintenance system model is established, PSO is used to optimize the model.
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.
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.
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.
微粒群算法是一种新型的智能优化算法，有较强的发现最优解的能力，且算法简单，易于实现。
Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement.
微粒群算法是一种新型的智能优化算法，有较强的发现最优解的能力，且算法简单，易于实现。
Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement.
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