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.
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