摘要
粒子群优化算法是一种基于群体智能理论的全局寻优算法。首先对粒子群优化算法的原理和实现过程进行了研究,然后比较了粒子群优化算法与粒子滤波算法的异同,并将粒子群优化算法引入到粒子滤波算法中,解决了粒子贫乏的问题。提高了每个粒子的作用效果,同时给出了PSO—PF算法的基本步骤。最后将PSO—PF算法应用于自航耙吸挖泥船的泥舱溢流损失估计中,采用实测工程数据进行了仿真。仿真结果表明该PSO—PF算法基本达到了预期的效果,为自航耙吸挖泥船操作人员的施工提供了决策支持。
Particle Swarm Optimization(PSO) is a global optimization algorithm based on swarm intelligence theory.Firstly,the theory and implementation process of PSO are researched.Then,the similarities and differences between PSO and PF are compared.Also,PSO is brought into PF,to solve the problem of particle-poor problem,which improves the effect of each particle.At the same time,the basic steps of PSO-PF are given.Lastly,PSO-PF is applied in hopper model of trailing suction hopper dredger,to estimate the overflow loss.In the text,real engineering data and MATLAB language are used to simulate.The simulation results are basically achieved the expected goal,which provides decision support for the operator's construction.
出处
《科学技术与工程》
北大核心
2012年第4期936-939,共4页
Science Technology and Engineering
关键词
粒子滤波
粒子群优化算法
自航耙吸挖泥船
溢流损失
particle filter particle swarm optimization trailing suction hopper dredger the overflow loss
