In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution(DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct...In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution(DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum,which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.展开更多
A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computation...A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.展开更多
The optimal test sequence design for fault diagnosis is a challenging NP-complete problem.An improved differential evolution(DE)algorithm with additional inertial velocity term called inertial velocity differential ev...The optimal test sequence design for fault diagnosis is a challenging NP-complete problem.An improved differential evolution(DE)algorithm with additional inertial velocity term called inertial velocity differential evolution(IVDE)is proposed to solve the optimal test sequence problem(OTP)in complicated electronic system.The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm.And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate(FIR)satisfied and generate diagnostic decision tree to decrease the test sets and the test cost.The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR.Compared with the other algorithms such as particle swarm optimization(PSO)and genetic algorithm(GA),IVDE can get better solution to OTP.展开更多
The differential evolution(DE)algorithm was deployed to calibrate microparameters of the DEM cohesive granular material.4 macroparameters,namely,uniaxial compressive strength,direct tensile strength,Young’s modulus a...The differential evolution(DE)algorithm was deployed to calibrate microparameters of the DEM cohesive granular material.4 macroparameters,namely,uniaxial compressive strength,direct tensile strength,Young’s modulus and Poisson’s ratio,can be calibrated to high accuracy.The best calibration accuracy could reach the sum of relative errors RE_(sum)<0.1%.Most calibrations can be achieved with RE_(sum)<5%within hours or RE_(sum)<1%within 2 days.Based on the calibrated results,microparameters uniqueness analysis was carried out to reveal the correlation between microparameters and the macroscopic mechanical behaviour of material:(1)microparameters effective modulus,tensile strength and normal-to-shear stiffness ratio control the elastic behaviour and stable crack growth,(2)microparameters cohesion and friction angles present a negative linear correlation that controls the axial strain and lateral strain prior to the peak stress,and(3)microparameters friction coefficient controls shear crack friction and slip mainly refers to the unstable crack behaviour.Consideration of more macroparameters to regulate the material mechanical behaviour that is dominated by shear crack and slip motion is highlighted for future study.The DE calibration method is expected to serve as an alternative method to calibrate the DEM cohesive granular material to its peak strength.展开更多
Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are we...Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.展开更多
To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the ...To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the worst at the beginning of each generation.Then,the population is partitioned into multiple levels,and different levels are used to exert different functions.In each level,a control parameter is used to select excellent exemplars from upper levels for learning.In this case,the poorer individuals can choose more learning exemplars to improve their exploration ability,and excellent individuals can directly learn from the several best individuals to improve the quality of solutions.To accelerate the convergence speed,a difference vector selection method based on the level is developed.Furthermore,specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process.A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.展开更多
The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE i...The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE is proposed.The population is divided into an elite subpopulation,an ordinary subpopulation,and an inferior subpopulation according to the fitness values.The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population,respectively.The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy.If the global optimum is not improved in a specified number of iterations,a tolerance mechanism is applied.Under the tolerance mechanism,the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy,respectively.In addition,the individual status and algorithm status are used to adaptively adjust the control parameters.To evaluate the performance of the proposed algorithm,six state-of-the-art DE algorithm variants are compared on the benchmark functions.The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.展开更多
In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata in...In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata into the DE algorithm,those interactions among vectors are restricted within cellular structure of neighbors while the cell own evolution,which may be used to balance the tradeoff between exploration and exploitation and then tune the selection pressure. And further more,the orthogonal crossover without crossover rate is used instead of the binomial crossover,which can maintain the population diversity and accelerate the convergence rate. Experimental studies are carried out on a suite of 7 bound-constrained numerical benchmark functions. The results show that the proposed algorithm has better capability of maintaining the population diversity and faster convergence than the classical differential evolution and several classic differential evolution variants.展开更多
Purpose-Feature selection is an important step for data pre-processing specially in the case of high dimensional data set.Performance of the data model is reduced if the model is trained with high dimensional data set...Purpose-Feature selection is an important step for data pre-processing specially in the case of high dimensional data set.Performance of the data model is reduced if the model is trained with high dimensional data set,and it results in poor classification accuracy.Therefore,before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.Design/methodology/approach-A novel optimization approach that hybridizes binary particle swarm optimization(BPSO)and differential evolution(DE)for fine tuning of SVM classifier is presented.The name of the implemented classifier is given as DEPSOSVM.Findings-This approach is evaluated using 20 UCI benchmark text data classification data set.Further,the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images.From the results,it can be observed that the proposed DEPSOSVMtechniques have significant improvement in performance over other algorithms in the literature for feature selection.The proposed technique shows better classification accuracy as well.Originality/value-The proposed approach is different from the previous work,as in all the previous work DE/(rand/1)mutation strategy is used whereas in this study DE/(rand/2)is used and the mutation strategy with BPSO is updated.Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function.The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier(DEPSOSVM)to handle the feature selection problems.展开更多
Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its conv...Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.展开更多
This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of...This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.展开更多
进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC...进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC控制方式的限制,容易发生振荡,因此提出一种基于差分进化(differential evolution,DE)和原—对偶内点法(primal-dual interior point method,PDIPM)的统一混合迭代算法。算法的主要思想是以DE算法为框架,对离散变量进行优化,在DE算法的每一次迭代过程中,采用PDIPM对每个DE个体进行连续变量的优化和适应度评估。由于采用PDIPM进行DE种群适应度评估,无需设定VSC-HVDC的控制方式,因此提高了算法的全局寻优能力。多个算例结果表明,该混合算法数值稳定性高,寻优能力强,能很好地解决含两端、多端、多馈入VSC-HVDC的交直流系统最优潮流问题。展开更多
基金supported by the JSPS KAKENHI(JP17K12751 and JP15K00332)
文摘In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution(DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum,which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.
基金funded by“The Pearl River Talent Recruitment Program”in 2019 for Professor Shui-Long Shen(Grant No.2019CX01G338),Guangdong Provincethe Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019)。
文摘A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.
基金supported by National Natural Science Foundation of Jiangxi Province, China (No. 20132BAB201044)Jiangxi Higher Technology Landing Project, China (No. KJLD12071)
文摘The optimal test sequence design for fault diagnosis is a challenging NP-complete problem.An improved differential evolution(DE)algorithm with additional inertial velocity term called inertial velocity differential evolution(IVDE)is proposed to solve the optimal test sequence problem(OTP)in complicated electronic system.The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm.And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate(FIR)satisfied and generate diagnostic decision tree to decrease the test sets and the test cost.The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR.Compared with the other algorithms such as particle swarm optimization(PSO)and genetic algorithm(GA),IVDE can get better solution to OTP.
文摘The differential evolution(DE)algorithm was deployed to calibrate microparameters of the DEM cohesive granular material.4 macroparameters,namely,uniaxial compressive strength,direct tensile strength,Young’s modulus and Poisson’s ratio,can be calibrated to high accuracy.The best calibration accuracy could reach the sum of relative errors RE_(sum)<0.1%.Most calibrations can be achieved with RE_(sum)<5%within hours or RE_(sum)<1%within 2 days.Based on the calibrated results,microparameters uniqueness analysis was carried out to reveal the correlation between microparameters and the macroscopic mechanical behaviour of material:(1)microparameters effective modulus,tensile strength and normal-to-shear stiffness ratio control the elastic behaviour and stable crack growth,(2)microparameters cohesion and friction angles present a negative linear correlation that controls the axial strain and lateral strain prior to the peak stress,and(3)microparameters friction coefficient controls shear crack friction and slip mainly refers to the unstable crack behaviour.Consideration of more macroparameters to regulate the material mechanical behaviour that is dominated by shear crack and slip motion is highlighted for future study.The DE calibration method is expected to serve as an alternative method to calibrate the DEM cohesive granular material to its peak strength.
基金This work was partially supported by the National Natural Science Foundation of China(62073173,61833011)the Natural Science Foundation of Jiangsu Province,China(BK20191376)the Nanjing University of Posts and Telecommunications(NY220193,NY220145)。
文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.
基金This work was supported in part by the National Natural Science Fund for Outstanding Young Scholars of China(No.61922072)the National Natural Science Foundation of China(Nos.61876169,61276238,61806179,and 61976237)Key Research and Development and Promotion Projects in Henan Province(No.192102210098).
文摘To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the worst at the beginning of each generation.Then,the population is partitioned into multiple levels,and different levels are used to exert different functions.In each level,a control parameter is used to select excellent exemplars from upper levels for learning.In this case,the poorer individuals can choose more learning exemplars to improve their exploration ability,and excellent individuals can directly learn from the several best individuals to improve the quality of solutions.To accelerate the convergence speed,a difference vector selection method based on the level is developed.Furthermore,specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process.A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.
基金This work was supported by the National Natural Science Foundation of China(Nos.61903089 and 62066019)the Natural Science Foundation of Jiangxi Province(Nos.20202BABL202020 and 20202BAB202014)the National Key Research and Development Program of China(No.2020YFB1713700).
文摘The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE is proposed.The population is divided into an elite subpopulation,an ordinary subpopulation,and an inferior subpopulation according to the fitness values.The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population,respectively.The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy.If the global optimum is not improved in a specified number of iterations,a tolerance mechanism is applied.Under the tolerance mechanism,the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy,respectively.In addition,the individual status and algorithm status are used to adaptively adjust the control parameters.To evaluate the performance of the proposed algorithm,six state-of-the-art DE algorithm variants are compared on the benchmark functions.The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.
基金Supported by the National Natural Science Foundation of China(No.61501186)the Jiangxi Province Science Foundation(No.20171BAB202001)the Visiting Scholar Foundation of Jiangxi Province Young and Middle-aged University Teachers'Development Program([2016],No.169)
文摘In order to reduce the pressure of parameter selection and avoid trapping into the local optimum,a novel differential evolution( DE) algorithm without crossover rate is proposed. Through embedding cellular automata into the DE algorithm,those interactions among vectors are restricted within cellular structure of neighbors while the cell own evolution,which may be used to balance the tradeoff between exploration and exploitation and then tune the selection pressure. And further more,the orthogonal crossover without crossover rate is used instead of the binomial crossover,which can maintain the population diversity and accelerate the convergence rate. Experimental studies are carried out on a suite of 7 bound-constrained numerical benchmark functions. The results show that the proposed algorithm has better capability of maintaining the population diversity and faster convergence than the classical differential evolution and several classic differential evolution variants.
文摘Purpose-Feature selection is an important step for data pre-processing specially in the case of high dimensional data set.Performance of the data model is reduced if the model is trained with high dimensional data set,and it results in poor classification accuracy.Therefore,before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.Design/methodology/approach-A novel optimization approach that hybridizes binary particle swarm optimization(BPSO)and differential evolution(DE)for fine tuning of SVM classifier is presented.The name of the implemented classifier is given as DEPSOSVM.Findings-This approach is evaluated using 20 UCI benchmark text data classification data set.Further,the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images.From the results,it can be observed that the proposed DEPSOSVMtechniques have significant improvement in performance over other algorithms in the literature for feature selection.The proposed technique shows better classification accuracy as well.Originality/value-The proposed approach is different from the previous work,as in all the previous work DE/(rand/1)mutation strategy is used whereas in this study DE/(rand/2)is used and the mutation strategy with BPSO is updated.Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function.The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier(DEPSOSVM)to handle the feature selection problems.
文摘Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.
文摘This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.
文摘进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC控制方式的限制,容易发生振荡,因此提出一种基于差分进化(differential evolution,DE)和原—对偶内点法(primal-dual interior point method,PDIPM)的统一混合迭代算法。算法的主要思想是以DE算法为框架,对离散变量进行优化,在DE算法的每一次迭代过程中,采用PDIPM对每个DE个体进行连续变量的优化和适应度评估。由于采用PDIPM进行DE种群适应度评估,无需设定VSC-HVDC的控制方式,因此提高了算法的全局寻优能力。多个算例结果表明,该混合算法数值稳定性高,寻优能力强,能很好地解决含两端、多端、多馈入VSC-HVDC的交直流系统最优潮流问题。
