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 individua...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.展开更多
将优化问题抽象成目标函数后,目标函数和启发式优化算法的匹配程度决定了优化求解的效率.为反映目标函数的优化特征并指导优化算法及其参数的选择,本文模拟绝热量子计算中的多基态演化,提出了一种适应度地形探索算法.根据基态波函数倾...将优化问题抽象成目标函数后,目标函数和启发式优化算法的匹配程度决定了优化求解的效率.为反映目标函数的优化特征并指导优化算法及其参数的选择,本文模拟绝热量子计算中的多基态演化,提出了一种适应度地形探索算法.根据基态波函数倾向于向势能较小处收敛且收敛程度受量子效应强度影响的特性,用目标函数编码势能场后算法引入了一个量子效应递减的多基态演化过程,用其持续收敛的基态波函数簇反映目标函数的适应度地形.根据量子路径积分,算法由尺度递减的扩散蒙特卡罗(diffusion Monte Carlo,DMC)实现.实验表明算法综合直观地反映了适应度地形的众多特征,所得信息能直接指导后续优化,其计算模式和启发式优化相似,无需引入其他计算,这为适应度地形研究引入了新的视角.展开更多
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘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.
文摘将优化问题抽象成目标函数后,目标函数和启发式优化算法的匹配程度决定了优化求解的效率.为反映目标函数的优化特征并指导优化算法及其参数的选择,本文模拟绝热量子计算中的多基态演化,提出了一种适应度地形探索算法.根据基态波函数倾向于向势能较小处收敛且收敛程度受量子效应强度影响的特性,用目标函数编码势能场后算法引入了一个量子效应递减的多基态演化过程,用其持续收敛的基态波函数簇反映目标函数的适应度地形.根据量子路径积分,算法由尺度递减的扩散蒙特卡罗(diffusion Monte Carlo,DMC)实现.实验表明算法综合直观地反映了适应度地形的众多特征,所得信息能直接指导后续优化,其计算模式和启发式优化相似,无需引入其他计算,这为适应度地形研究引入了新的视角.
