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。
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。
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。
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