The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.T...The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.This paper proposes a combined power generation cycle(single flash geothermal cycle with trans-critical CO_(2) cycle)and simulates in the EES(Engineering Equation Solver)software.The results show that the design parameters of the proposed system are significantly improved compared to the BASIC single flash cycle.Then,the proposed approach is optimized using the genetic algorithm and the Nelder-Mead Simplex method.Separator pressure,steam turbine output pressure,and CO_(2) turbine inlet pressure are three assumed variable parameters,and exergy efficiency is the target parameter.In the default operating mode,the system exergy efficiency was 32%,increasing to 39%using the genetic algorithm and 37%using the Nelder-Mead method.展开更多
The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-line...The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.展开更多
This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,c...This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.展开更多
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency ...A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.展开更多
Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA...Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.展开更多
This paper works on a modified simplex algorithm for the local optimization of Continuous Piece Wise Linear（CPWL） programming with generalization of hinging hyperplane objective and linear constraints. CPWL programm...This paper works on a modified simplex algorithm for the local optimization of Continuous Piece Wise Linear（CPWL） programming with generalization of hinging hyperplane objective and linear constraints. CPWL programming is popular since it can be equivalently transformed into difference of convex functions programming or concave optimization. Inspired by the concavity of the concave CPWL functions, we propose an Objective Variation Simplex Algorithm（OVSA）, which is able to find a local optimum in a reasonable time. Computational results are presented for further insights into the performance of the OVSA compared with two other algorithms on random test problems.展开更多
On December 7,2022,the Chinese government optimized the current epidemic prevention and control policy,and no longer adopted the zero-COVID policy and mandatory quarantine measures.Based on the above policy changes,th...On December 7,2022,the Chinese government optimized the current epidemic prevention and control policy,and no longer adopted the zero-COVID policy and mandatory quarantine measures.Based on the above policy changes,this paper establishes a compartment dynamics model considering age distribution,home isolation and vaccinations.Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data.Then,using the estimated parameter values to predict a second wave of the outbreak,the peak of severe cases will reach on 8 May 2023,the number of severe cases will reach 206,000.Next,it is proposed that with the extension of the effective time of antibodies obtained after infection,the peak of severe cases in the second wave of the epidemic will be delayed,and the final scale of the disease will be reduced.When the effectiveness of antibodies is 6 months,the severe cases of the second wave will peak on July 5,2023,the number of severe cases is 194,000.Finally,the importance of vaccination rates is demonstrated,when the vaccination rate of susceptible people under 60 years old reaches 98%,and the vaccination rate of susceptible people over 60 years old reaches 96%,the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023,when the number of severe cases is 166,000.展开更多
基金Yashar Aryanfar is receiving a scholarship from the National Council of Science and Technology(CONACYT)of Mexico to pursue his doctoral studies at the Universidad Autonoma de Ciudad Juarez under Grant No.1162359.
文摘The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.This paper proposes a combined power generation cycle(single flash geothermal cycle with trans-critical CO_(2) cycle)and simulates in the EES(Engineering Equation Solver)software.The results show that the design parameters of the proposed system are significantly improved compared to the BASIC single flash cycle.Then,the proposed approach is optimized using the genetic algorithm and the Nelder-Mead Simplex method.Separator pressure,steam turbine output pressure,and CO_(2) turbine inlet pressure are three assumed variable parameters,and exergy efficiency is the target parameter.In the default operating mode,the system exergy efficiency was 32%,increasing to 39%using the genetic algorithm and 37%using the Nelder-Mead method.
基金This manuscript is supported by the National Natural Science Foundation of China(No.42174011,41874001 and 42174011).
文摘The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LTGS23E070001).
文摘This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.
基金Supported by the Natural Science Foundation of Jiangsu Province (No.BK2004016).
文摘A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LY13F020044 and LZ14F030004)the National Natural Science Foundation of China(No.61571170)
文摘Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.
基金supported by the National Natural Science Foundation of China (Nos. 61473165 and 61134012)the National Key Basic Research and Development (973) Program of China (No. 2012CB720505)
文摘This paper works on a modified simplex algorithm for the local optimization of Continuous Piece Wise Linear（CPWL） programming with generalization of hinging hyperplane objective and linear constraints. CPWL programming is popular since it can be equivalently transformed into difference of convex functions programming or concave optimization. Inspired by the concavity of the concave CPWL functions, we propose an Objective Variation Simplex Algorithm（OVSA）, which is able to find a local optimum in a reasonable time. Computational results are presented for further insights into the performance of the OVSA compared with two other algorithms on random test problems.
基金supported by the National Natural Science Foundation of China(12022113 and 12271314)Henry Fok Foundation for Young Teachers(171002)Outstanding Young Talents Support Plan of Shanxi Province.
文摘On December 7,2022,the Chinese government optimized the current epidemic prevention and control policy,and no longer adopted the zero-COVID policy and mandatory quarantine measures.Based on the above policy changes,this paper establishes a compartment dynamics model considering age distribution,home isolation and vaccinations.Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data.Then,using the estimated parameter values to predict a second wave of the outbreak,the peak of severe cases will reach on 8 May 2023,the number of severe cases will reach 206,000.Next,it is proposed that with the extension of the effective time of antibodies obtained after infection,the peak of severe cases in the second wave of the epidemic will be delayed,and the final scale of the disease will be reduced.When the effectiveness of antibodies is 6 months,the severe cases of the second wave will peak on July 5,2023,the number of severe cases is 194,000.Finally,the importance of vaccination rates is demonstrated,when the vaccination rate of susceptible people under 60 years old reaches 98%,and the vaccination rate of susceptible people over 60 years old reaches 96%,the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023,when the number of severe cases is 166,000.
