However, the standard Particle Swarm Optimization is easy to fall into local optimum, and slow convergence.
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。
Based on the standard particle swarm optimization algorithm, a new algorithm using uniform design to determining parameters is presented in this paper.
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。
Compared with standard Particle Swarm Optimization in four typical test functions, results show that our algorithm has potential to find a better solution.
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。
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。
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.
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 (PSO) algorithm cannot adapt to the complex and nonlinear optimization process, because the same inertia weight is used to update the velocity of particles.
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。
Because of this, a particle swarm optimization algorithm with the strategy of nonlinear decreasing inertia weight is proposed based on the standard particle swarm algorithm.
结果提出一种非线性递减惯性权重策略的粒子群优化算法。
To improve the search quality of the standard PSO algorithm for solving high-dimensional function, a dynamic particle swarm optimization algorithm is proposed.
针对基本粒子群优化算法对高维函数优化时搜索精度不高的缺陷，提出了一种动态粒子群优化算法。
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 ;
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 ;
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；
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