标准粒子群算法易陷入局部最优值。
Standard particle swarm algorithm is easy to fall into local optimum.
然而，标准粒子群算法存在容易陷入局部最优，后期收敛过慢等问题。
However, the standard Particle Swarm Optimization is easy to fall into local optimum, and slow convergence.
在标准粒子群算法中引入非线性变化权重和变异操作来保证全局收敛并提高收敛精度。
By introducing the nonlinear variation weight and mutational operation into the standard particle swarm algorithm to ensuring the overall convergence and enhance the accuracy of convergence.
标准测试函数的特性与选择，改进粒子群算法与标准粒子群算法的比较实验与结果分析；
The features and options of Standard test functions, The comparative experiment and the results analyzing of the improved standard particle swarm algorithm and particle swarm optimization ;
在标准粒子群算法的基础上，针对关键参数经验设置法的局限性，提出了一种新的粒子群算法。
Based on the standard particle swarm optimization algorithm, a new algorithm using uniform design to determining parameters is presented in this paper.
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。
Firstly the thesis introduce several improved PSO algorithm and standard PSO which are from basic PSO.
与标准粒子群算法在四个典型测试函数上进行了比较，实验结果表明该算法有很好的潜力找到更好的解。
Compared with standard Particle Swarm Optimization in four typical test functions, results show that our algorithm has potential to find a better solution.
标准的粒子群优化算法作为一种随机全局搜索算法，因其在种群中传播速度过快，易陷入局部最优解。
The standard particle swarm optimization algorithm as a random global search algorithm, because of its rapid propagation in populations, easily into the local optimal solution.
由于标准粒子群优化(PSO)算法把惯性权值作为全局参数，因此很难适应复杂的非线性优化过程。
The standard particle Swarm optimization (PSO) algorithm cannot adapt to the complex and nonlinear optimization process, because the same inertia weight is used to update the velocity of particles.
本文在阅读了大量有关粒子群算法的文献的基础上，对标准的粒子群算法研究和分析。
In this paper, consulted a lot of literature about particle swarm optimization algorithm, and did research and analysis on the standard particle swarm optimization.
本文在阅读了大量有关粒子群算法的文献的基础上，对标准的粒子群算法研究和分析。
In this paper, consulted a lot of literature about particle swarm optimization algorithm, and did research and analysis on the standard particle swarm optimization.
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