Firstly the thesis introduce several improved PSO algorithm and standard PSO which are from basic PSO.
本论文首先介绍了经过基本粒子群算法改进而来的标准粒子群算法以及改进的其他PSO算法。
Guaranteed by the indentation factor, the improved PSO algorithm can keep the iteration particles in feasible region.
通过引入缩进因子，改进P SO算法，使粒子在迭代过程中保持在可行域内。
The results show that the improved PSO has the advantages of improving premature and the prediction effect is raised.
结果表明，改进的粒子群算法具有改善早熟现象的优点，且预测效果有所提高。
Result shows that the improved PSO is brief and effective, and enhances the reconnaissance efficiency of the UAVs greatly.
结果表明，该算法简单有效，在很大程度上提高了无人机的侦察效率。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
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。
For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
在此基础上，进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
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.
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。
Application examples show that it is feasible to apply the improved PSO to the weight solution of power load combination forecasting model.
通过应用实例证明，将改进的粒子群优化算法应用到电力负荷组合预测模型的权重求解是可行的。
The results indicate the validity of improved PSO methods in the back analysis of the mechanical parameters of concrete face rock-fill dam.
计算结果表明，微粒群算法具有概念简单、容易实现、计算速度快等优点，适合应用于面板堆石坝坝料参数反演分析。
The structure of multi-layer feedback forward neural network is optimized by improved PSO. Learning quality and training speed of the neural network are improved.
提出的自适应粒子群优化算法，用于优化多层前馈神经网络的拓扑结构，提高了神经网络的学习质量和速度。
The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.
在基准函数的测试中，结果显示ADPSO算法比其他PSO算法有更好的运行效果，是求解多峰问题的一种有效算法。
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。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
It USES the dynamic scale-free like network as the particle's optimization neighborhood. It proposes an improved PSO algorithm based on variety inertia weight and dynamic neighborhood.
将有向动态类无标度网作为粒子寻优邻域，提出一种基于变惯性权重及动态邻域的改进P SO算法。
As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.
为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷，提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。
The improved PSO is then applied to optimal design of disk brake in order to minimize the brake time under the conditions of geometry constrain, intensity constrain and temperature constrain.
应用此方法，以制动时间最短为目标，在几何约束、强度约束、温度约束等条件下，对盘式制动器的主要设计参数进行了优化设计。
The improved PSO algorithm is tested in two models, to show the effects of improvement and its ability in solving the inverse time overcurrent relay coordination problem when DG units are connected.
通过对两个算例的分析可看到改进算法的效果以及粒子群算法解决分布式电源并网情况下反时限过流保护整定问题的能力。
A method based on improved binary particle swarm optimization (PSO) is proposed for distribution network reconfiguration with the objective of load balancing.
提出了基于改进的二进制粒子群优化算法、以均衡负荷为目标的配电网重构方法。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
This paper reviews the basic version and many kinds of improved versions of PSO, and future research issues are also given.
该文综述了算法的基本形式及其多种改进形式，并给出了未来可能的研究方向。
The improved particle swarm optimization (PSO) is used to optimize the PID controller parameters.
应用改进的粒子群优化算法优化PID参数。
The economic dispatch of a small microgird example in island mode was optimized by improved particle swarm optimization PSO method, simulation results show the effectiveness of the proposed method.
采用改进粒子群算法对孤岛运行模式下的一个小型的微网系统算例进行研究，仿真计算结果表明了所提方法的有效性。
The proposed model is solved by improved particle swarm optimization (PSO) algorithm.
针对此模型，采用改进粒子群优化算法进行求解。
Experimental results show that the improved algorithm performs better than the traditional PSO and may avoid falling into the local optimum instead.
实验结果证明，与传统PSO算法相比，改进算法的寻优效果较好，可在一定程度上避免陷入局部最优。
We formulated scenario based multi-objective stochastic programming model to describe the problem of capacity planning under uncertainty and applied improved Multi-Objective PSO (MOPSO) to solve it.
提出了基于场景的多目标随机规划模型来构建不确定市场需求环境下的能力计划问题模型，并用改进的多目标粒子群优化算法求解。
Compared to standard PSO, its global searching ability and the speed of convergence is significantly improved, and the premature convergence problem is effectively avoided.
与标准微粒群算法相比，算法的全局搜索能力和收敛速度都得到了显著提高，同时能够有效避免早熟收敛。
Second, an Enhanced Phase Locked Loop (EPLL) control strategy based on improved Adaptive Notch Filtering (ANF) is proposed, controller parameters are optimized using BF-PSO algorithm.
其次，提出了一种基于改进型ANF的三相EPLL控制策略，并用BF-PSO算法对控制器参数进行优化设计。
Second, an Enhanced Phase Locked Loop (EPLL) control strategy based on improved Adaptive Notch Filtering (ANF) is proposed, controller parameters are optimized using BF-PSO algorithm.
其次，提出了一种基于改进型ANF的三相EPLL控制策略，并用BF-PSO算法对控制器参数进行优化设计。
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