In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation mo...In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency was established, and then, a new Tabu search and particle swarm hybrid optimizing algorithm was proposed to find solutions. While actual data of energy demand and distribution in China were selected for analysis, the economic critical value in comparison between the long-distance coal transfer and electric power transmission was gained. Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methods that are based on regional price differences, freight rates and annual cost with the proposed method, the result indicates that the economic efficiency of the energy transfer can be enhanced by 3.14%, 5.78% and 6.01%, respectively.展开更多
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.展开更多
基金Project(20050079008) supported by the Specialized Research Fund for the Doctoral Program of Higher Education
文摘In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency was established, and then, a new Tabu search and particle swarm hybrid optimizing algorithm was proposed to find solutions. While actual data of energy demand and distribution in China were selected for analysis, the economic critical value in comparison between the long-distance coal transfer and electric power transmission was gained. Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methods that are based on regional price differences, freight rates and annual cost with the proposed method, the result indicates that the economic efficiency of the energy transfer can be enhanced by 3.14%, 5.78% and 6.01%, respectively.
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
基金Project(52005358)supported by the National Natural Science Foundation of ChinaProject(2018YFB1307902)supported by the National Key R&D Program of China+1 种基金Project(201901D111243)supported by the Natural Science Foundation of Shanxi Province,ChinaProject(2019-KF-25-05)supported by the Natural Science Foundation of Liaoning Province,China。
文摘To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
