With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampl...With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampling survey of large grain farmers in 15 counties and cities of northeast China, with the aid of SPSS and AMOS software, using multiple regression analysis and structural equation modeling, this paper made a quantitative analysis on the influence of the subjective and ob- jective factors of large grain farmers on their large-scale management. The results showed that the age structure, educational level, family operating capital, yield ex- pectation and protective farming awareness of large grain farmers are the positive factors influencing their large scale operation due to agricultural subsidy policy. By comparison, the number of agricultural machinery and equipment owned by family, regional labor force, expectation for future income, and expectation for contractual scale become negative factors influencing large-scale operation of large grain farm- ers because of agricultural policies. When the future expectation, self conditions, family endowment, and operation conditions of large grain farmers increase one unit, their large scale operation motivation will increase by 0.692, 0.689, 0.487 and 0.363 units respectively. Thus, increasing the future expectation and self conditions of large grain farmers is a key factor for promoting large scale operation of farmland.展开更多
Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes...Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.展开更多
Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Al...Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.展开更多
At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a pr...At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.展开更多
The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes th...The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes the data source of index. Secondly, the weight of index is determined and the fuzzy comprehensive evaluation model is proposed. Finally, results of instance analysis show that the evaluation model is feasible and effective.展开更多
Let G be a locally compact Vilenkin gro up . We will establish the boundedness in Morrey spaces L p,λ (G) for a la rge class of sublinear operators and linear commutators.
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a singl...In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.展开更多
The strong type and weak type estimates of parameterized Littlewood-Paley operators on the weighted Herz spaces Kq α,p（ω1,ω2） are considered. The boundednessof the commutators generated by BMO functions and param...The strong type and weak type estimates of parameterized Littlewood-Paley operators on the weighted Herz spaces Kq α,p（ω1,ω2） are considered. The boundednessof the commutators generated by BMO functions and parameterized Littlewood-Paley operators are also obtained.展开更多
To deal with the radio frequency threat posed by modern complex radar networks to aircraft,we researched the unmanned aerial vehicle(UAV)formations radar countermeasures,aiming at the solution of radar jamming resourc...To deal with the radio frequency threat posed by modern complex radar networks to aircraft,we researched the unmanned aerial vehicle(UAV)formations radar countermeasures,aiming at the solution of radar jamming resource allocation under system countermeasures.A jamming resource allocation method based on an improved firefly algorithm(FA)is proposed.Firstly,the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation.Besides,the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model.Finally,A mutation operator and an adaptive heuristic are integtated into the FA algorithm,which searches an interference resource allocation scheme.The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm.The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network.Moreover,in terms of convergence accuracy and speed as well as algorithm stability,the improved FA algorithm is superior to the simulated annealing algorithm(SA),the niche genetic algorithm(NGA),the improved discrete cuckoo algorithm(IDCS),the mutant firefly algorithm(MFA),the cuckoo search and fireflies algorithm(CSFA),and the best neighbor firefly algorithm(BNFA).展开更多
With the load growth and the power grid expansion,the problem of short-circuit current(SCC)exceeding the secure limit in large-scale power grids has become more serious,which poses great challenge to the optimal secur...With the load growth and the power grid expansion,the problem of short-circuit current(SCC)exceeding the secure limit in large-scale power grids has become more serious,which poses great challenge to the optimal secure operation.Aiming at the SCC limitations,we use multiple back-toback voltage source converter based(B2B VSC)systems to separate a large-scale AC power grid into two asynchronous power grids.A multi-objective robust optimal secure operation model of large-scale power grid with multiple B2B VSC systems considering the SCC limitation is established based on the AC power flow equations.The decision variables include the on/off states of synchronous generators,power output,terminal voltage,transmission switching,bus sectionalization,and modulation ratios of B2B VSC systems.The influence of inner current sources of renewable energy generators on the system SCC is also considered.To improve the computational efficiency,a mixedinteger convex programming(MICP)framework based on convex relaxation methods including the inscribed N-sided approximation for the nonlinear SCC limitation constraints is proposed.Moreover,combined with the column-and-constraint generation(C&CG)algorithm,a method to directly solve the compromise optimal solution(COS)of the multi-objective robust optimal secure operation model is proposed.Finally,the effectiveness and computational efficiency of the proposed solution method is demonstrated by an actual 4407-bus provincial power grid and the modified IEEE 39-bus power grid,which can reduce the consumed CPU time of solving the COS by more than 90%and obtain a better COS.展开更多
The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study...The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some extent, can result in premature convergence of genes (PCG) in the same locus. The above drawback of the traditional mutation operator causes the loss of critical alleles. Inspired by digital technique, we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can effectively prevent premature convergence, and can provide a wide selection range of control parameters for GA.展开更多
This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear c...This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.展开更多
Base editing,the targeted introduction of point mutations into cellular DNA,holds promise for improving genome-scale functional genome screening to single-nucleotide resolution.Current efforts in prokaryotes,however,r...Base editing,the targeted introduction of point mutations into cellular DNA,holds promise for improving genome-scale functional genome screening to single-nucleotide resolution.Current efforts in prokaryotes,however,remain confined to loss-of-function screens using the premature stop codons-mediated gene inactivation library,which falls far short of fully releasing the potential of base editors.Here,we developed a base editor-mediated functional single nucleotide variant screening pipeline in Escherichia coli.We constructed a library with 31,123 sgRNAs targeting 462 stress response-related genes in E.coli,and screened for adaptive mutations under isobutanol and furfural selective conditions.Guided by the screening results,we successfully identified several known and novel functional mutations.Our pipeline might be expanded to the optimization of other phenotypes or the strain engineering in other microorganisms.展开更多
文摘With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampling survey of large grain farmers in 15 counties and cities of northeast China, with the aid of SPSS and AMOS software, using multiple regression analysis and structural equation modeling, this paper made a quantitative analysis on the influence of the subjective and ob- jective factors of large grain farmers on their large-scale management. The results showed that the age structure, educational level, family operating capital, yield ex- pectation and protective farming awareness of large grain farmers are the positive factors influencing their large scale operation due to agricultural subsidy policy. By comparison, the number of agricultural machinery and equipment owned by family, regional labor force, expectation for future income, and expectation for contractual scale become negative factors influencing large-scale operation of large grain farm- ers because of agricultural policies. When the future expectation, self conditions, family endowment, and operation conditions of large grain farmers increase one unit, their large scale operation motivation will increase by 0.692, 0.689, 0.487 and 0.363 units respectively. Thus, increasing the future expectation and self conditions of large grain farmers is a key factor for promoting large scale operation of farmland.
基金the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-RP23030).
文摘Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.
基金supported by the National Natural Science Foundation of China(Nos.62303013,62276001,and U21A20512).
文摘Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.
文摘At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.
基金supported by the Natural Science Foundation of China(71704184)Projects of the of the National Social Science Foundation of China(15GJ003-245)Science Foundation of Equipment Research(JJ20172A05095)
文摘The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes the data source of index. Secondly, the weight of index is determined and the fuzzy comprehensive evaluation model is proposed. Finally, results of instance analysis show that the evaluation model is feasible and effective.
文摘Let G be a locally compact Vilenkin gro up . We will establish the boundedness in Morrey spaces L p,λ (G) for a la rge class of sublinear operators and linear commutators.
基金This work was supported by the National Natural Science Foundation of China (No50335030)
文摘In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.
文摘The strong type and weak type estimates of parameterized Littlewood-Paley operators on the weighted Herz spaces Kq α,p（ω1,ω2） are considered. The boundednessof the commutators generated by BMO functions and parameterized Littlewood-Paley operators are also obtained.
文摘To deal with the radio frequency threat posed by modern complex radar networks to aircraft,we researched the unmanned aerial vehicle(UAV)formations radar countermeasures,aiming at the solution of radar jamming resource allocation under system countermeasures.A jamming resource allocation method based on an improved firefly algorithm(FA)is proposed.Firstly,the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation.Besides,the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model.Finally,A mutation operator and an adaptive heuristic are integtated into the FA algorithm,which searches an interference resource allocation scheme.The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm.The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network.Moreover,in terms of convergence accuracy and speed as well as algorithm stability,the improved FA algorithm is superior to the simulated annealing algorithm(SA),the niche genetic algorithm(NGA),the improved discrete cuckoo algorithm(IDCS),the mutant firefly algorithm(MFA),the cuckoo search and fireflies algorithm(CSFA),and the best neighbor firefly algorithm(BNFA).
基金supported by the National Natural Science Foundation of China(No.51977080).
文摘With the load growth and the power grid expansion,the problem of short-circuit current(SCC)exceeding the secure limit in large-scale power grids has become more serious,which poses great challenge to the optimal secure operation.Aiming at the SCC limitations,we use multiple back-toback voltage source converter based(B2B VSC)systems to separate a large-scale AC power grid into two asynchronous power grids.A multi-objective robust optimal secure operation model of large-scale power grid with multiple B2B VSC systems considering the SCC limitation is established based on the AC power flow equations.The decision variables include the on/off states of synchronous generators,power output,terminal voltage,transmission switching,bus sectionalization,and modulation ratios of B2B VSC systems.The influence of inner current sources of renewable energy generators on the system SCC is also considered.To improve the computational efficiency,a mixedinteger convex programming(MICP)framework based on convex relaxation methods including the inscribed N-sided approximation for the nonlinear SCC limitation constraints is proposed.Moreover,combined with the column-and-constraint generation(C&CG)algorithm,a method to directly solve the compromise optimal solution(COS)of the multi-objective robust optimal secure operation model is proposed.Finally,the effectiveness and computational efficiency of the proposed solution method is demonstrated by an actual 4407-bus provincial power grid and the modified IEEE 39-bus power grid,which can reduce the consumed CPU time of solving the COS by more than 90%and obtain a better COS.
文摘The mutation operator has been seldom improved because researchers hardly suspect its ability to prevent genetic algorithm (GA) from converging prematurely. Due to its importance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some extent, can result in premature convergence of genes (PCG) in the same locus. The above drawback of the traditional mutation operator causes the loss of critical alleles. Inspired by digital technique, we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can effectively prevent premature convergence, and can provide a wide selection range of control parameters for GA.
文摘This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.
基金supported by the National Key Research and Development Program of China (2018YFA0901500)the National Natural Science Foundation of China (U2032210)。
文摘Base editing,the targeted introduction of point mutations into cellular DNA,holds promise for improving genome-scale functional genome screening to single-nucleotide resolution.Current efforts in prokaryotes,however,remain confined to loss-of-function screens using the premature stop codons-mediated gene inactivation library,which falls far short of fully releasing the potential of base editors.Here,we developed a base editor-mediated functional single nucleotide variant screening pipeline in Escherichia coli.We constructed a library with 31,123 sgRNAs targeting 462 stress response-related genes in E.coli,and screened for adaptive mutations under isobutanol and furfural selective conditions.Guided by the screening results,we successfully identified several known and novel functional mutations.Our pipeline might be expanded to the optimization of other phenotypes or the strain engineering in other microorganisms.
