Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical opti...Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.展开更多
A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz...A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.展开更多
It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. No...It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor＇s algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory（OCT） is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor＇s algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.展开更多
A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： dispers...A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.展开更多
Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of ...Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of development is established. The model is solved by improved simultaneous perturbation stochastic approximation algorithm(SPSA), and an automatic optimization software for liquid production is developed. This model avoids the disadvantage of traditional optimization methods that only focus on the maximum value of mathematics but ignore the production law of oilfield. It has the advantages of high efficiency of calculation, short period and automatic optimization. It can satisfy the automatic optimization of liquid production in later stage of oilfield development. The software was applied in the oilfield development of D oilfield, Ecuador in South America, and realized the automatic optimization of liquid production in the later stage of oilfield development.展开更多
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
基金supported by UGC SRF Fellowship(NTA Ref.Nos.211610092425 and 201610165233).
文摘Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.
基金the National Natural Science Foundation of China (No.50579007)
文摘A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.
基金supported by the National Natural Science Foundation of China(Grant No.61205108)the High Performance Computing(HPC)Foundation of National University of Defense Technology,China
文摘It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor＇s algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory（OCT） is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor＇s algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.
基金the National Natural Science Foundation of China (70572045).
文摘A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.
基金Supported by the China National Science and Technology Major Project(2016ZX05031-001)
文摘Based on the optimal control theory and taking the production law of reservoirs with strong natural aquifer as the basic constraint, a mathematical model of liquid production for such reservoirs in the later stage of development is established. The model is solved by improved simultaneous perturbation stochastic approximation algorithm(SPSA), and an automatic optimization software for liquid production is developed. This model avoids the disadvantage of traditional optimization methods that only focus on the maximum value of mathematics but ignore the production law of oilfield. It has the advantages of high efficiency of calculation, short period and automatic optimization. It can satisfy the automatic optimization of liquid production in later stage of oilfield development. The software was applied in the oilfield development of D oilfield, Ecuador in South America, and realized the automatic optimization of liquid production in the later stage of oilfield development.
