A modified Trigonometric Differential Evolution algorithm for optimization of dynamic systems

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4630986

Rakesh Angira, A. Santosh

{"title":"A modified Trigonometric Differential Evolution algorithm for optimization of dynamic systems","authors":"Rakesh Angira, A. Santosh","doi":"10.1109/CEC.2008.4630986","DOIUrl":null,"url":null,"abstract":"Differential evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of trigonometric differential evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the modified trigonometric differential evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2008.4630986","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

Abstract

Differential evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of trigonometric differential evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the modified trigonometric differential evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms.

查看原文本刊更多论文

一种用于动态系统优化的改进三角微分进化算法

差分进化(DE)是一种处理不可微、非线性和多模态目标函数的新型进化算法。已有研究表明，DE是一种高效、稳健的进化优化方法。然而，求解计算代价昂贵的目标函数(例如计算力学、计算流体动力学、最优控制等领域的优化问题)仍然需要大量的计算时间，因此，尝试加速DE是必要的。本文讨论了一种改进的三角微分进化(TDE)算法的应用和评价。对TDE算法进行了改进，进一步提高了算法的收敛速度。在此基础上，对改进的三角微分进化(MTDE)算法在化工动态优化问题中的应用进行了评价。将MTDE算法与TDE算法和原始DE算法的性能进行了比较。结果表明，MTDE算法比TDE和DE算法效率高，且速度明显快于前者。

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

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

自引率

0.00%

发文量