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

作　　者：范勤勤 王循华 颜学峰 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)

出　　处：《Journal of Central South University》2015年第6期2227-2237,共11页中南大学学报（英文版）

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

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.

关 键 词：harmony search differential evolution optimization CO-EVOLUTION self-adaptive control parameter dynamic optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TQ021.8[自动化与计算机技术—控制科学与工程]