The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. ...The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.展开更多
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti...An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj...The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.展开更多
In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high ...In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high rank schemas at the subsequent generation decrease exponentially even though its fitness is more optimal than the average one in the population and the low rank schemas at the subsequent generation increase exponentially when its fitness is more optimal than the average one in the population. In order to overcome the shortcoming that the optimal high rank schema can be deserted arbitrarily, the HGA (hybrid partheno-genetic algorithm) is proposed, that is, the hill-climbing algorithm is integrated to search for a better individual. Finally, the results of the simulation for facility layout problem and no-wait schedule problem are given. It is shown that the hybrid partheno- genetic algorithm is of high efficiency.展开更多
为更精确地预测航班过站时间,将全国机场按照规模差异及不同地理位置所导致的客流量差异和天气差异对航班过站时间造成的不同影响进行分类,基于各类机场航班数据,构建混合轻量级梯度提升机算法(LightGBM)模型对航班过站时间分类预测。...为更精确地预测航班过站时间,将全国机场按照规模差异及不同地理位置所导致的客流量差异和天气差异对航班过站时间造成的不同影响进行分类,基于各类机场航班数据,构建混合轻量级梯度提升机算法(LightGBM)模型对航班过站时间分类预测。引入自适应鲁棒损失函数(adaptive robust loss function,ARLF)改进LightGBM模型损失函数,降低航班数据中存在离群值的影响;通过改进的麻雀搜索算法对改进后的LightGBM模型进行参数寻优,形成混合LightGBM模型。采用全国2019年全年航班数据进行验证,实验结果验证了方法的可行性。展开更多
基金supported in part by the National Outstanding Youth Foundation of P.R.China (60525303)the National Natural Science Foundation of P.R.China(60404022,60604004)+2 种基金the Natural Science Foundation of Hebei Province (102160)the special projects in mathematics funded by the Natural Science Foundation of Hebei Province(07M005)the NS of Education Office in Hebei Province (2004123).
文摘The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.
基金Projects(61174040,61104178,61374136) supported by the National Natural Science Foundation of ChinaProject(12JC1403400) supported by Shanghai Commission of Science and Technology,ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of China
文摘The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
文摘In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high rank schemas at the subsequent generation decrease exponentially even though its fitness is more optimal than the average one in the population and the low rank schemas at the subsequent generation increase exponentially when its fitness is more optimal than the average one in the population. In order to overcome the shortcoming that the optimal high rank schema can be deserted arbitrarily, the HGA (hybrid partheno-genetic algorithm) is proposed, that is, the hill-climbing algorithm is integrated to search for a better individual. Finally, the results of the simulation for facility layout problem and no-wait schedule problem are given. It is shown that the hybrid partheno- genetic algorithm is of high efficiency.
文摘为更精确地预测航班过站时间,将全国机场按照规模差异及不同地理位置所导致的客流量差异和天气差异对航班过站时间造成的不同影响进行分类,基于各类机场航班数据,构建混合轻量级梯度提升机算法(LightGBM)模型对航班过站时间分类预测。引入自适应鲁棒损失函数(adaptive robust loss function,ARLF)改进LightGBM模型损失函数,降低航班数据中存在离群值的影响;通过改进的麻雀搜索算法对改进后的LightGBM模型进行参数寻优,形成混合LightGBM模型。采用全国2019年全年航班数据进行验证,实验结果验证了方法的可行性。
