But the PSO algorithms are easily trapped into local optimization.
但粒子群调度算法容易陷入局部最优。
Particle Swarm Optimization(PSO)is used to select parameters for the model.
用粒子群算法对融合模型进行优化得到PSO优化模型。
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。
PSO is a new evolutionary algorithm and is simple in structure and easy to operate.
PSO算法是一种新的仿生优化方法，具有结构和运算简单的优点。
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。
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。
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。
Furthermore, PSO was modified here to improve the solving precision and convergent rates of optimization procedure.
并对普通粒子群优化算法进行了改进，提高了优化过程的求解精度和收敛速度。
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) 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。
This paper adds mutation operator to adaptive PSO and apply it in the lymphoma morphology parameter classifier problems.
对自适应粒子群算法引入变异算子，并对其进行改进，将其应用到淋巴瘤形态参数的分类问题上。
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。
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。
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。
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。
The results show that IPSO can improve the global convergence performance of traditional PSO greatly, heighten the accuracy of the solution.
计算实例表明改进型粒子群优化算法大大改善了传统PSO算法的全局收敛性能，解的精度提高了很多。
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。
The algorithm divides the whole search space into a number of sub-spaces, which are optimized by utilizing the PSO embedded zero search operators.
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。
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