文献类型：期刊文献

中文题名：Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

英文题名：Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

作者：Fan Qin-qin[1];Wang Xun-hua[1];Yan Xue-feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2015

卷号：22

期号：6

起止页码：2227

中文期刊名：Journal of Central South University

外文期刊名：JOURNAL OF CENTRAL SOUTH UNIVERSITY

收录：CSTPCD;;EI(收录号：20152400940882);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000356043700026)】；CSCD:【CSCD2015_2016】；

基金：Foundation item: Project(2013CB733605) supported by the National Basic Research Program of China; Project(21176073) supported by the National Natural Science Foundation of China

语种：英文

中文关键词：harmony search; differential evolution optimization; co-evolution; self-adaptive control parameter; dynamic optimization

外文关键词：harmony search; differential evolution optimization; co-evolution; self-adaptive control parameter; dynamic optimization

摘要：A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.