Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex
来源期刊：中南大学学报(英文版)2013年第4期
论文作者：FAN Qin-qin(范勤勤) LÜ Zhao-min(吕照民) YAN Xue-feng(颜学峰) GUO Mei-jin(郭美锦)
文章页码：950 - 959
Key words：evolutionary computation; dynamic optimization; differential evolution algorithm; Alopex algorithm; self-adaptivity
Abstract: To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
FAN Qin-qin(范勤勤), LÜ Zhao-min(吕照民), YAN Xue-feng(颜学峰), GUO Mei-jin(郭美锦)
(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:To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
Key words:evolutionary computation; dynamic optimization; differential evolution algorithm; Alopex algorithm; self-adaptivity
