采用改进混合粒子群算法的热工过程模型参数辨识  

Model Identification of Thermal Process Based on Modified Hybrid Particle Swarm Optimization

作　　者：陈真[1] 王明春[1] 张雨飞[1] 

机构地区：[1]东南大学能源与环境学院,南京210096

出　　处：《发电设备》2017年第4期223-226,共4页Power Equipment

摘　　要：针对基本粒子群算法在求解过程中容易陷入局部最优解的问题,提出了一种基于遗传算法和模拟退火算法而改进的粒子群算法。引入遗传算法中的选择、杂交和变异,以及模拟退火机制的粒子群算法,在保持群体多样性的同时,提高全局搜索速度,并将改进的算法应用到热工过程模型参数的辨识,试验结果显示改进效果良好。To solve the problem of basic particle swarm optimization algorithm that is easy to fall into the local optimal solution, a modified hybrid particle swarm optimization algorithm was proposed based on the genetic algorithm and simulated annealing algorithm, i.e. introducing the selection, crossover and mutation in the genetic algorithm and the particle swarm optimization in the simulated annealing mechanism, which is able to maintain the diversity of particle swarms and improve the global evolution speed. Application results show that the modified hybrid particle swarm optimization algorithm has better effects compared with the basic particle swarm optimization algorithm.

关 键 词：系统辨识 粒子群算法 遗传算法 模拟退火算法 

分 类 号：TK321[动力工程及工程热物理—热能工程]