The algorithm discards the particle velocity and tracks the state of particle swarm by observing the overall changes in the fitness of all particles in the swarm.
该算法舍弃了粒子速度这个参数，并通过粒子群中所有粒子适应度的整体变化跟踪粒子群的状态。
Aiming at the stagnation exists in the cooperative particle swarm optimization, presents a new kind of the cooperative particle swarm optimization algorithm based on particles spatial extension.
针对协同微粒群优化存在的停滞现象，提出了一种新的基于粒子空间扩展的协同微粒群优化算法。
During the running time, the particles are updated through the combination form of chaos update and particle swarm update so that the global convergence could balance against local convergence.
在算法运行过程中，对粒子的位置进行混沌更新和粒子群更新相结合的更新方式，使全局收敛与局部收敛达到一定平衡。
This paper presents swarm optimization algorithm based on a pair of parallel particles, which can be used to get a good codebook in the vector quantization of image coding.
本文提出一种粒子群分组并行寻优码书设计算法，应用于图像的矢量量化编码中，它可以得到性能较好的码书。
To solve a class of non-differentiable optimization problems, this paper proposed a new method called maximum-entropy particles swarm optimization algorithm.
针对一类不可微优化问题，本文提出了一个新的算法—极大熵微粒群混合算法。
The experiment results show that particles swarm optimization is an effective method for parameter estimation of system model.
实验结果表明，该算法是一种有效的系统模型参数估计方法。
Each iteration, some bad particles of one sub-swarm are replaced with some good particles of another under a substituting probability.
搜索时，每一次迭代均以一定的替代率用一分群中若干优势微粒取代另一分群中相同数目的劣势微粒。
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。
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。
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