Here, I'm going to show how PSO can be used to minimize functions.
在这里，我将展示如何粒子群可用于最小化的功能。
It was combined grid theory with Particle Swarm Optimization (PSO).
它是结合网格原理与粒子群算法(PSO)。
Particle swarm Optimization (PSO) algorithm is based on swarm intelligence theory.
粒子群优化(PSO)算法是基于群体智能理论的优化算法。
Particle Swarm Optimization (PSO) is such a new artificial life computation method.
粒子群优化(PSO)算法是其中较新的一种人工生命计算方法。
Specific particle swarm optimization (PSO) algorithm is designed to solve the model.
针对模型的特点设计了求解模型的特殊PSO算法。
Particle Swarm Optimization(PSO)algorithm is one of embranchments of swarm intelligence.
粒子群优化算法是群体智能中一个新的分支。
A method of image restoration based on PSO and simulated annealing algorithm is proposed.
提出了基于微粒群和模拟退火算法的图像复原算法。
Especially in high noise disturbance condition, the results of PSO are also satisfactory.
尤其是在高噪信比情况下，也收到较满意的结果。
A discrete Particle Swarm Optimization (PSO) algorithm was presented for Job Shop scheduling problem.
提出了用于解决作业车间调度问题的离散版粒子群算法。
The results illustrate that the new PSO has higher performance than the PSO with global inertia weight.
使用四个不同类型基准函数测试结果表明，新型算法比全局惯性权值算法性能更好。
In order to speed up convergence, this paper implants the memory mechanism in the traditional binary PSO.
为了加快粒子群算法的收敛速度，论文在传统粒子群算法中引入了记忆机制。
Particle Swarm Optimization (PSO) algorithm has existed premature convergence for multimodal search problems.
粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。
Using the PSO algorithm to optimize the on-line PID controller's parameters, desirable control effect is obtained.
通过运用PSO算法对PID控制器参数进行在线调整，使模型参考自适应控制达到理想的控制效果。
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。
Particle Swarm optimization (PSO) is an evolutionary computation technique and an optimization tool based on iteration.
粒子群优化算法(PSO)是一种进化计算技术，是一种基于迭代的优化工具。
An effective method for solving this problem is to obtain the information of PSO for controlling PSO evolution process.
针对这一问题，通过获取粒子群的状态信息，来控制PSO进化过程，是一种有效的P SO改进方法。
The paper studies the mathematical model of the dynamic reactive optimization and the particle swarm optimization (PSO).
论文研究了动态无功优化的数学模型和粒子群优化算法。
A new boundary mutation strategy, the minimum boundary mutation, is presented based on Particle Swarm Optimization (PSO).
提出一种新的粒子群算法(PSO)边界变异策略——最小值边界变异。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
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 optimizer (PSO) is a new evolutionary computation method, which has been successfully applied to many fields.
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。
The experimental results show that the performance of the proposed parallel algorithm is better than that of the standard PSO.
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。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
针对K均值聚类算法依赖于初始值的选择，且容易收敛于局部极值的缺点，提出一种基于粒群优化的K均值算法。
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。
Particle Swarm Optimization (PSO) algorithm for solving multiple Nash equilibrium solutions of bimatrix game is presented in this paper.
提出了一种求解双矩阵对策多重纳什均衡解的粒子群优化算法。
A slope stability evaluation method based on particle swarm optimization (PSO) and least square support vector machine (LSSVM) is proposed.
提出了基于粒子群算法(PSO)和最小二乘支持向量机(LSSVM)的边坡稳定性评价方法。
Particle swarm optimization (PSO) is a new stochastic optimization technique originating from artificial life and evolutionary computation.
粒子群优化(PSO)算法是一类新兴的随机优化技术，其思想来源于人工生命和演化计算理论。
In order to improve its performance, the paper puts forward a hybrid algorithm which blends the PSO algorithm and simulated annealing algorithm.
为了提高优化性能，将粒子群算法和模拟退火算法结合，得到了粒子群-模拟退火混合调度算法。
In order to improve its performance, the paper puts forward a hybrid algorithm which blends the PSO algorithm and simulated annealing algorithm.
为了提高优化性能，将粒子群算法和模拟退火算法结合，得到了粒子群-模拟退火混合调度算法。
应用推荐