针对传统粒子群算法(Traditional Particle Swarm Optimization,TPSO)存在的易陷入局部最优、收敛速度慢等缺点,提出了一种基于载波的粒子群算 ..
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针对传统粒子群算法 TPSO ; Traditional Particle Swarm Optimization
传统粒子群优化算法 particle swarm optimization
传统的粒子群优化算法 particle swarm optimiza-tion
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.
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