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
构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutiona...构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutionary algorithm,AEA)中,利用分布估计算法收敛速度快及与传统进化算法进化模式不同的特点来改进AEA算法。新算法综合了AEA算法搜索得到的个体间相关性信息和EDA搜索过程中得到的全局概率信息,能够更好地指导种群向有利的区域进化。仿真结果表明:EDA改进的EDA-Alopex算法搜索性能与AEA算法的搜索性能相比有较大提高,特别是其收敛速度与AEA算法相比有明显提高。展开更多
Based on an Alopex optimization algorithm and a response surface model(RSM),a hybrid sub-region methodology is presented to solve the optimal design problems of permanent magnet(PM)machines.The Alopex optimization met...Based on an Alopex optimization algorithm and a response surface model(RSM),a hybrid sub-region methodology is presented to solve the optimal design problems of permanent magnet(PM)machines.The Alopex optimization method is processed both in subspace and in global solution space.In order to decrease the computing time,a multi quadric radial basis function(MQRBF)is embedded in the optimization.The proposed method speeds up the convergence rate while keeps the accuracy of the solution.A numerical experiment is given to validate the efficiency and effectiveness of the method.展开更多
Untanpreeda presented a training algorithm based on BP [1] , which guarantees the closed loop stability for a class of widely used Neural network control systems. However, it has some shortcomings, such as insuf...Untanpreeda presented a training algorithm based on BP [1] , which guarantees the closed loop stability for a class of widely used Neural network control systems. However, it has some shortcomings, such as insufficient stable condition, low efficiency and frequent convergence of parameters to a local minimum. A new training algorithm based on Alopex is proposed to ensure sufficient stability, and overcome some of the shortcomings.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.
文摘构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutionary algorithm,AEA)中,利用分布估计算法收敛速度快及与传统进化算法进化模式不同的特点来改进AEA算法。新算法综合了AEA算法搜索得到的个体间相关性信息和EDA搜索过程中得到的全局概率信息,能够更好地指导种群向有利的区域进化。仿真结果表明:EDA改进的EDA-Alopex算法搜索性能与AEA算法的搜索性能相比有较大提高,特别是其收敛速度与AEA算法相比有明显提高。
文摘Based on an Alopex optimization algorithm and a response surface model(RSM),a hybrid sub-region methodology is presented to solve the optimal design problems of permanent magnet(PM)machines.The Alopex optimization method is processed both in subspace and in global solution space.In order to decrease the computing time,a multi quadric radial basis function(MQRBF)is embedded in the optimization.The proposed method speeds up the convergence rate while keeps the accuracy of the solution.A numerical experiment is given to validate the efficiency and effectiveness of the method.
文摘Untanpreeda presented a training algorithm based on BP [1] , which guarantees the closed loop stability for a class of widely used Neural network control systems. However, it has some shortcomings, such as insufficient stable condition, low efficiency and frequent convergence of parameters to a local minimum. A new training algorithm based on Alopex is proposed to ensure sufficient stability, and overcome some of the shortcomings.
