Modified differential evolution algorithm and its application in thermal process model identification

2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering Pub Date : 2010-11-01 DOI:10.1109/ISKE.2010.5680830

Changliang Liu, Ming-yang Yu

{"title":"Modified differential evolution algorithm and its application in thermal process model identification","authors":"Changliang Liu, Ming-yang Yu","doi":"10.1109/ISKE.2010.5680830","DOIUrl":null,"url":null,"abstract":"The mathematical model of the object in power plant is of extremely significance for the design and analysis of the thermal control system. There are many methods to identify the parameters of the desiring object. In this article, we adopt a modified form of a relatively effective yet simple algorithm called differential evolution algorithm (DE) which is a population based stochastic optimization approach. The differential evolution algorithm uses the difference of randomly sampled pairs of vectors in the population for its mutation operators and is applied mainly in real parameter optimization. Based on analysis of DE searching mechanism, the article proposed the improved differential evolution algorithm with self-adaptive parameters to promote its robust, optima searching capability and speed. In order to prove the effectiveness of the improved differential evolution algorithm, we work out relevant model identifying program on MATLAB and identify the mathematical models. Then we analyze the result using the method of comparing.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"26 1","pages":"450-453"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2010.5680830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

Abstract

The mathematical model of the object in power plant is of extremely significance for the design and analysis of the thermal control system. There are many methods to identify the parameters of the desiring object. In this article, we adopt a modified form of a relatively effective yet simple algorithm called differential evolution algorithm (DE) which is a population based stochastic optimization approach. The differential evolution algorithm uses the difference of randomly sampled pairs of vectors in the population for its mutation operators and is applied mainly in real parameter optimization. Based on analysis of DE searching mechanism, the article proposed the improved differential evolution algorithm with self-adaptive parameters to promote its robust, optima searching capability and speed. In order to prove the effectiveness of the improved differential evolution algorithm, we work out relevant model identifying program on MATLAB and identify the mathematical models. Then we analyze the result using the method of comparing.

查看原文本刊更多论文

改进差分进化算法及其在热工过程模型识别中的应用

火电厂热力控制对象的数学模型对热力控制系统的设计和分析具有十分重要的意义。有许多方法可以确定期望对象的参数。在本文中，我们采用了一种相对有效而简单的算法的改进形式，称为差分进化算法(DE)，这是一种基于种群的随机优化方法。差分进化算法利用种群中随机采样的向量对的差异作为变异算子，主要应用于实参数优化。在分析DE搜索机制的基础上，提出了一种自适应参数的改进差分进化算法，提高了算法的鲁棒性、最优搜索能力和速度。为了证明改进的差分进化算法的有效性，在MATLAB上编写了相应的模型识别程序，并对数学模型进行了识别。然后采用比较法对结果进行分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering

自引率

0.00%

发文量