在此基础上，进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
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.
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 new adaptive mutation particle swarm optimizer (AMPSO), which is based on the variance of the population's fitness is presented.
提出了一种基于带有变异算子的改进粒子群算法对二冷区水量进行优化的新方法。
A novel method based on modified particle swarm optimization (IPSO) with mutation algorithm was proposed to optimize water quantity in secondary cooling process in the continuous slab casting.
采用变异粒子群优化算法确定了模型参数，并取得了较好的效果。
The mutation particle swarm optimization algorithm is employed to determine the constitutive parameters and it is proved to present good performance.
提出一种新的粒子群算法(PSO)边界变异策略——最小值边界变异。
A new boundary mutation strategy, the minimum boundary mutation, is presented based on Particle Swarm Optimization (PSO).
对自适应粒子群算法引入变异算子，并对其进行改进，将其应用到淋巴瘤形态参数的分类问题上。
This paper adds mutation operator to adaptive PSO and apply it in the lymphoma morphology parameter classifier problems.
但是粒子群算法没有遗传操作如交叉和变异，而是根据自己的速度来决定搜索。粒子还有一个重要的特点是记忆。
But the particle swarm without the inheritance operation such as cross and variation, decided the searching according to its speed, and particle has an important memory character.
在对基本的算法的分析比较基础上，分析了一种基于克隆的免疫遗传算法、基于高低位变异的免疫算法、基于粒子群优化的免疫算法。
On this basis, I analyze Immune Algorithm based on Clone, Immune Algorithm based on High and Low Position and Immune Algorithm based on Particle Swarm Optimization.
该算法在运行过程中增加了随机变异算子，通过对当前最佳粒子进行随机变异来增强粒子群优化算法跳出局部最优解的能力。
The new algorithm includes the mutation operator during the running time which can be useful to improve the ability of PSO in breaking away from the local optimum.
该算法在运行过程中增加了随机变异算子，通过对当前最佳粒子进行随机变异来增强粒子群优化算法跳出局部最优解的能力。
The new algorithm includes the mutation operator during the running time which can be useful to improve the ability of PSO in breaking away from the local optimum.
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