摘要
粒子群优化算法,起源于鸟群行为的研究,是一种基于群智能的进化计算技术,通过粒子之间的协作与竞争以实现对多维复杂空间的高效搜索。提出了基于Petri网的并行粒子群算法,并采用经典测试函数验证算法的有效性。测试结果表明,算法能很好地控制粒子群优化过程中的早熟问题,并能够较好地得到群落全局最优解。
Particle swarm optimization,rooting from simulation of swarm of birds, is a new branch of evolution algorithms based on swarm intelligence,realizing effective search on multi-dimension complex spaces through cooperation and competition between particles.The paper defines a parallel particles warm optimization algorithm based on Petri net.Experiment results demonstrate that the algorithm has bigger speed of convergence and better optimizing result compared with the other particles warm optimization.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第31期54-56,共3页
Computer Engineering and Applications
基金
湖南省自然科学基金No.08JJ3124~~
关键词
PETRI网
并行理论
并行粒子群算法
Petri net
parallel theory
parallel particles warm optimization algorithm
