基于极值动力学机制和信息融合搜索的混合算法及其应用  

A hybrid algorithm based on extremal optimization with adaptive lévy mutation and information fashion algorithm and its applications

作　　者：付晓刚[1] 俞金寿[2] 

机构地区：[1]上海电机学院电气学院,上海200240 [2]华东理工大学自动化研究所,上海200237

出　　处：《化工学报》2011年第8期2355-2359,共5页CIESC Journal

基　　金：国家自然科学基金项目(60772006)~~

摘　　要：提出了一种新的基于自适应lévy变异的极值动力学和信息融合搜索的混合算法。新算法将全局搜索和局部搜索机制有机地结合起来,在全局搜索过程中,信息融合搜索算法(IFA)作为一种群智能进化算法,能够快速地逼近近似最优解;在局部搜索过程中,通过选择近似解的最差组元进行自适应lévy变异,利用极值动力学算法(EO)强大的局部搜索能力,协助IFA跳出局部极值点。将其运用于超临界水氧化去除率神经网络软测量建模,实验结果表明了方法的有效性和实用性。A hybrid algorithm based on extremal optimization（EO）with adaptive lévy mutation and information fashion algorithm（EOIFA）was proposed in this paper.It applied the idea of combination mechanism of global and local search.In the process of the global search,IFA is an evolutionary algorithm based on the difference in group that can quickly approach an approximate optimal solution.EO has powerful local search capability.During the local search,it helps IFA out of local maximum points by selecting the approximate solution of the worst element to proceed adaptive lévy mutation.The EOIFA is applied to train artificial neural network（NN）to construct a practical soft-sensor of removal efficiencies for supercritical water oxidation.The obtained results indicate that the new method proposed by this paper is feasible and effective.

关 键 词：信息融合搜索算法 极值动力学优化算法 自适应lévy变异 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]