简化E．Coli觅食优化算法及其在非线性模型参数辨识中的应用

Simplified E.Coli foraging optimization algorithm and its application to parameter identification of nonlinear system model

针对非线性系统模型参数辨识问题,提出了一种简化E.Coli觅食优化算法,该算法是基于大肠杆菌(E.Coli)的化学趋向性行为,包含翻转算子和游动算子。同时还采用个体历史最佳位置和群体最佳位置对E.Coli群体进行更新(优值跟踪算子).以重油热解三集总非线性模型参数估计验证了简化E.Coli觅食优化算法的效果,实验结果表明了简化E.Coli觅食优化算法的有效性,为非线性系统模型参数估计提供了一种新方法.

A simplified E.Coli foraging optimization algorithm based on the chemotaetic behavior of E.Coli is presented in this paper for the parameter estimation problem of nonlinear system model （NSM）. The simplified E.Coli algorithm includes a tumbling operator, a swimming operator, the optimal position of individual E.Coli and the location of all E.Coli swarm are adopted to update the locations of swarm （tracing operator of optimal values）. The effectiveness of the proposed simplified E.Coli foraging optimization algorithm is demonstrated by simulation experiments on the parameter estimation of the NSM for heavy oil thermal cracking. The results show that the simplified E.Coli model is valid and provides an attractive method to the estimation of parameters of NSM.