为了加快粒子群算法的收敛速度，论文在传统粒子群算法中引入了记忆机制。
In order to speed up convergence, this paper implants the memory mechanism in the traditional binary PSO.
与传统粒子群算法相比，它更具有生物特性，更接近于鸟群觅食的真实规律。
Comparing with the traditional PSO algorithm, it possesses more biological characteristics and is much more closed to the real rules of birds swarm's foraging.
计算实例表明改进型粒子群优化算法大大改善了传统PSO算法的全局收敛性能，解的精度提高了很多。
The results show that IPSO can improve the global convergence performance of traditional PSO greatly, heighten the accuracy of the solution.
实验证明，这种基于多粒子群的跟踪算法可以应用于实时视频跟踪，其跟踪效果优于传统算法。
The simulation results show that the presented multiple particle swarms algorithm of video tracking is able to apply in real time, and the tracking performance is superior to conventional algorithms.
实验证明，这种基于多粒子群的跟踪算法可以应用于实时视频跟踪，其跟踪效果优于传统算法。
The simulation results show that the presented multiple particle swarms algorithm of video tracking is able to apply in real time, and the tracking performance is superior to conventional algorithms.
应用推荐