Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization
来源期刊：中南大学学报(英文版)2015年第6期
论文作者：FAN Qin-qin WANG Xun-hua YAN Xue-feng
文章页码：2227 - 2237
Key words：harmony search; differential evolution optimization; co-evolution; self-adaptive control parameter; dynamic optimization
Abstract: 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.
FAN Qin-qin(范勤勤), WANG Xun-hua(王循华), YAN Xue-feng(颜学峰)
(Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education
(East China University of Science and Technology), Shanghai 200237, China)
Abstract: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.
Key words:harmony search; differential evolution optimization; co-evolution; self-adaptive control parameter; dynamic optimization
