摘要
粒子群优化(PSO)算法是一种群集智能方法,它通过粒子之间的合作与竞争以实现对多维复杂空间的高效搜索。在对于粒子群群体构造和粒子多样性对收敛速度和精度影响的研究基础上提出了一种改进型粒子群优化算法。针对工程中的有约束的优化问题,将改进粒子群算法与函数法相结合进行求解。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。
Particle swarm optimization （PSO） is a kind of swarm intelligence method. The particle swarm optimization is an algorithm for searching the multidimensional complex space efficiently through cooperation and competition among the individuals in a population of particles. Based on the research on the affection of population construction and the difference of particles to convergence speed and precision,an improved PSO （IPSO） algorithm is proposed. For the constrained optimization problems in Engineering,IPSO is combined with punish function to get the optimum. The results show that IPSO can improve the global convergence performance of traditional PSO greatly,heighten the accuracy of the solution.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2005年第9期984-987,990,共5页
Chinese Journal of Scientific Instrument
关键词
粒子群优化算法
工程优化
种群构造
粒子多样性
Particle swarm optimization Engineer optimization Population construction Difference of particles
