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
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
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.
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
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
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
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
多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization o...多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization of decision variables,MOFA-GD)。引入决策变量分组机制,根据各变量对算法性能的不同影响,将整体决策变量划分成收敛性变量组和多样性变量组;设计决策变量分组优化模型,利用学习行为优化收敛性变量组,加快种群收敛速度,非均匀变异算子优化多样性变量组,避免种群过早收敛,逐渐减小的变异幅度引导种群局部开发,提升算法寻优精度;采用档案截断策略维护外部档案,精准删除拥挤个体,从而保持外部档案的多样性。实验结果表明:MOFA-GD表现出优秀的收敛速度和寻优精度,获得了均匀分布的Pareto解集。本文所提算法为求解多目标优化问题提供了一种高效且可靠的解决方案。展开更多
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.
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.
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.
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.
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.
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.
文摘多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization of decision variables,MOFA-GD)。引入决策变量分组机制,根据各变量对算法性能的不同影响,将整体决策变量划分成收敛性变量组和多样性变量组;设计决策变量分组优化模型,利用学习行为优化收敛性变量组,加快种群收敛速度,非均匀变异算子优化多样性变量组,避免种群过早收敛,逐渐减小的变异幅度引导种群局部开发,提升算法寻优精度;采用档案截断策略维护外部档案,精准删除拥挤个体,从而保持外部档案的多样性。实验结果表明:MOFA-GD表现出优秀的收敛速度和寻优精度,获得了均匀分布的Pareto解集。本文所提算法为求解多目标优化问题提供了一种高效且可靠的解决方案。
