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。
A new adaptive mutation particle swarm optimizer (AMPSO), which is based on the variance of the population's fitness is presented.
本文提出了一种新的基于群体适应度方差自适应变异的粒子群优化算法(AMPSO)。
A short-term load forecasting model based on SVM is presented in which the parameters in SVM are optimized by Particle Swarm Optimizer (PSO).
文章提出了PSO优化参数的SVM回归预测模型，并将其用于短期电力负荷预测。
The proposed algorithm is almost as simple for implement as particle swarm optimizer, but can improve the abilities of seeking the global excellent result and evolution speed.
该算法基本保持了粒子群优化算法简单容易实现的特点，但改善了粒子群优化算法摆脱局部极值点的能力，提高了算法的收敛速度和精度。
This thesis introduces Discrete Particle Swarm Optimizer (DPSO) Algorithm to solve the fleet assignment problem. Firstly, the characterization of the fleet assignment is analyzed.
本文将进化算法引入飞机排班问题，研究并实现了基于离散型粒子群算法的飞机排班系统。
So multimodal problem has turn into unimodal problem in some clusters in which the optimal solution is guaranteedly located through the guaranteed convergence particle swarm optimizer.
此时问题已转化为多个簇的单模态问题，在各个簇中再利用保收敛微粒群优化器获得每个簇的最优解。
So multimodal problem has turn into unimodal problem in some clusters in which the optimal solution is guaranteedly located through the guaranteed convergence particle swarm optimizer.
此时问题已转化为多个簇的单模态问题，在各个簇中再利用保收敛微粒群优化器获得每个簇的最优解。
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