This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide...This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.展开更多
Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is ...Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).展开更多
Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitati...Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.展开更多
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua...To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.展开更多
Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, and has been widely used in both benchmark test functions and re...Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, and has been widely used in both benchmark test functions and real-world applications. This paper introduces a novel mutation operator, without using the scaling factor F, a conventional control parameter, and this mutation can generate multiple trial vectors by incorporating different weighted values at each generation, which can make the best of the selected multiple parents to improve the probability of generating a better offspring. In addition, in order to enhance the capacity of adaptation, a new and adaptive control parameter, i.e. the crossover rate CR, is presented and when one variable is beyond its boundary, a repair rule is also applied in this paper. The proposed algorithm ADE is validated on several constrained engineering design optimization problems reported in the specialized literature. Compared with respect to algorithms representative of the state-of-the-art in the area, the experimental results show that ADE can obtain good solutions on a test set of constrained optimization problems in engineering design.展开更多
To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic i...To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual,and each original individual has its own symbiotic individual. Differential evolution( DE) operators are used to evolve the original population. And,particle swarm optimization( PSO) is applied to co-evolving the symbiotic population. Thus,with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functions. The results show that the average performance of PSODE is the best.展开更多
Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of ...Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of water resources is essential. One of the important parameters in agriculture field is water distribution network. In this research, differential evolution algorithm (DE) was used to optimize Ismail Abad water supply network. This network is pressurized network and includes 19 pipes and 18 nodes. Optimization of the network has been evaluated by developing an optimization model based on DE algorithm in MATLAB and the dynamic connection with EPANET software for network hydraulic calculation. The developing model was run for the scale factor (F), the crossover constant (Cr), initial population (N) and the number of generations (G) and was identified best adeptness for DE algorithm is 0.6, 0.5, 100 and 200 for F and Cr, N and G, respectively. The optimal solution was compared with the classical empirical method and results showed that implementation cost of the network by DE algorithm was 10.66% lower than the classical empirical method.展开更多
A projected skill is adopted by use of the differential evolution (DE) algorithm to calculate a conditional nonlinear optimal perturbation (CNOP). The CNOP is the maximal value of a constrained optimization problem wi...A projected skill is adopted by use of the differential evolution (DE) algorithm to calculate a conditional nonlinear optimal perturbation (CNOP). The CNOP is the maximal value of a constrained optimization problem with a constraint condition, such as a ball constraint. The success of the DE algorithm lies in its ability to handle a non-differentiable and nonlinear cost function. In this study, the DE algorithm and the traditional optimization algorithms used to obtain the CNOPs are compared by analyzing a theoretical grassland ecosystem model and a dynamic global vegetation model. This study shows that the CNOPs generated by the DE algorithm are similar to those by the sequential quadratic programming (SQP) algorithm and the spectral projected gradients (SPG2) algorithm. If the cost function is non-differentiable, the CNOPs could also be caught with the DE algorithm. The numerical results suggest the DE algorithm can be employed to calculate the CNOP, especially when the cost function is non-differentiable.展开更多
This paper presents an improved gravitational search algorithm (IGSA) as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm (GSA), with the free search differential...This paper presents an improved gravitational search algorithm (IGSA) as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm (GSA), with the free search differential evolution (FSDE). This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.展开更多
In this paper, the authors show that the general linear second order ordinary Differential Equation can be formulated as an optimization problem and that evolutionary algorithms for solving optimization problems can a...In this paper, the authors show that the general linear second order ordinary Differential Equation can be formulated as an optimization problem and that evolutionary algorithms for solving optimization problems can also be adapted for solving the formulated problem. The authors propose a polynomial based scheme for achieving the above objectives. The coefficients of the proposed scheme are approximated by an evolutionary algorithm known as Differential Evolution (DE). Numerical examples with good results show the accuracy of the proposed method compared with some existing methods.展开更多
Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differentia...Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.展开更多
The Differential Evolution (DE) algorithm is arguably one of the most powerful stochastic optimization algorithms, which has been widely applied in various fields. Global numerical optimization is a very important and...The Differential Evolution (DE) algorithm is arguably one of the most powerful stochastic optimization algorithms, which has been widely applied in various fields. Global numerical optimization is a very important and extremely dif-ficult task in optimization domain, and it is also a great need for many practical applications. This paper proposes an opposition-based DE algorithm for global numerical optimization, which is called GNO2DE. In GNO2DE, firstly, the opposite point method is employed to utilize the existing search space to improve the convergence speed. Secondly, two candidate DE strategies “DE/rand/1/bin” and “DE/current to best/2/bin” are randomly chosen to make the most of their respective advantages to enhance the search ability. In order to reduce the number of control parameters, this algorithm uses an adaptive crossover rate dynamically tuned during the evolutionary process. Finally, it is validated on a set of benchmark test functions for global numerical optimization. Compared with several existing algorithms, the performance of GNO2DE is superior to or not worse than that of these algorithms in terms of final accuracy, convergence speed, and robustness. In addition, we also especially compare the opposition-based DE algorithm with the DE algorithm without using the opposite point method, and the DE algorithm using “DE/rand/1/bin” or “DE/current to best/2/bin”, respectively.展开更多
An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution...An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution algorithm. The algorithm adopts a nonlinear convergence strategy, a crossover strategy of differential evolution and the introduction of an elimination mechanism, which balances the global search and local exploitation ability of the algorithm and improves the accuracy of the solved optimal solution. The 13-unit and 40-unit systems are selected for economic load distribution calculation, and the experimental results show that the proposed improved algorithm is superior in distributing the economic load of the power system and can effectively reduce the economic cost.展开更多
A novel and simple technique to control the search direction of the differential mutation was proposed.In order to verify the performance of this method,ten widely used benchmark functions were chosen and the results ...A novel and simple technique to control the search direction of the differential mutation was proposed.In order to verify the performance of this method,ten widely used benchmark functions were chosen and the results were compared with the original differential evolution(DE)algorithm.Experimental results indicate that the search direction controlled DE algorithm obtains better results than the original DE algorithm in term of the solution quality and convergence rate.展开更多
This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing ...This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.展开更多
This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers th...This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.展开更多
The differential evolution(DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems(WDSs).This paper aims to carry out a comprehensive performance compa...The differential evolution(DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems(WDSs).This paper aims to carry out a comprehensive performance comparison between the new emerged DE algorithm and the most popular algorithm-the genetic algorithm(GA).A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454.A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison.It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study.Additionally,the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies,indicating that the DE exhibits comparable performance with other algorithms.It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs.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
Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find t...Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization(IWO_DE/Ring) is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process(MOGP) is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.展开更多
基金supported by the National Natural Science Foundation of China(61973105,62373137)。
文摘This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
文摘Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).
基金Supported by National Natural Science Foundation of China(Grant No.51175029)Beijing Municipal Natural Science Foundation of China(Grant No.3132019)
文摘Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
文摘Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, and has been widely used in both benchmark test functions and real-world applications. This paper introduces a novel mutation operator, without using the scaling factor F, a conventional control parameter, and this mutation can generate multiple trial vectors by incorporating different weighted values at each generation, which can make the best of the selected multiple parents to improve the probability of generating a better offspring. In addition, in order to enhance the capacity of adaptation, a new and adaptive control parameter, i.e. the crossover rate CR, is presented and when one variable is beyond its boundary, a repair rule is also applied in this paper. The proposed algorithm ADE is validated on several constrained engineering design optimization problems reported in the specialized literature. Compared with respect to algorithms representative of the state-of-the-art in the area, the experimental results show that ADE can obtain good solutions on a test set of constrained optimization problems in engineering design.
基金National Key Basic Research Project of China(973 program)(No.2013CB733600)National Natural Science Foundation of China(No.21176073)+1 种基金Program for New Century Excellent Talents in University,China(No.NCET-09-0346)the Fundamental Research Funds for the Central Universities,China
文摘To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual,and each original individual has its own symbiotic individual. Differential evolution( DE) operators are used to evolve the original population. And,particle swarm optimization( PSO) is applied to co-evolving the symbiotic population. Thus,with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functions. The results show that the average performance of PSODE is the best.
文摘Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of water resources is essential. One of the important parameters in agriculture field is water distribution network. In this research, differential evolution algorithm (DE) was used to optimize Ismail Abad water supply network. This network is pressurized network and includes 19 pipes and 18 nodes. Optimization of the network has been evaluated by developing an optimization model based on DE algorithm in MATLAB and the dynamic connection with EPANET software for network hydraulic calculation. The developing model was run for the scale factor (F), the crossover constant (Cr), initial population (N) and the number of generations (G) and was identified best adeptness for DE algorithm is 0.6, 0.5, 100 and 200 for F and Cr, N and G, respectively. The optimal solution was compared with the classical empirical method and results showed that implementation cost of the network by DE algorithm was 10.66% lower than the classical empirical method.
基金provided by grants from the National Basic Research Program of China (Grant No. 2006CB400503)LASG Free Exploration Fund+1 种基金LASG State Key Laboratory Special Fundthe KZCX3-SW-230 of the Chinese Academy of Sciences
文摘A projected skill is adopted by use of the differential evolution (DE) algorithm to calculate a conditional nonlinear optimal perturbation (CNOP). The CNOP is the maximal value of a constrained optimization problem with a constraint condition, such as a ball constraint. The success of the DE algorithm lies in its ability to handle a non-differentiable and nonlinear cost function. In this study, the DE algorithm and the traditional optimization algorithms used to obtain the CNOPs are compared by analyzing a theoretical grassland ecosystem model and a dynamic global vegetation model. This study shows that the CNOPs generated by the DE algorithm are similar to those by the sequential quadratic programming (SQP) algorithm and the spectral projected gradients (SPG2) algorithm. If the cost function is non-differentiable, the CNOPs could also be caught with the DE algorithm. The numerical results suggest the DE algorithm can be employed to calculate the CNOP, especially when the cost function is non-differentiable.
基金supported by the National Natural Science Foundation of China (70871081)the Shanghai Leading Academic Discipline Project of China (S1205YLXK)
文摘This paper presents an improved gravitational search algorithm (IGSA) as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm (GSA), with the free search differential evolution (FSDE). This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.
文摘In this paper, the authors show that the general linear second order ordinary Differential Equation can be formulated as an optimization problem and that evolutionary algorithms for solving optimization problems can also be adapted for solving the formulated problem. The authors propose a polynomial based scheme for achieving the above objectives. The coefficients of the proposed scheme are approximated by an evolutionary algorithm known as Differential Evolution (DE). Numerical examples with good results show the accuracy of the proposed method compared with some existing methods.
文摘Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.
文摘The Differential Evolution (DE) algorithm is arguably one of the most powerful stochastic optimization algorithms, which has been widely applied in various fields. Global numerical optimization is a very important and extremely dif-ficult task in optimization domain, and it is also a great need for many practical applications. This paper proposes an opposition-based DE algorithm for global numerical optimization, which is called GNO2DE. In GNO2DE, firstly, the opposite point method is employed to utilize the existing search space to improve the convergence speed. Secondly, two candidate DE strategies “DE/rand/1/bin” and “DE/current to best/2/bin” are randomly chosen to make the most of their respective advantages to enhance the search ability. In order to reduce the number of control parameters, this algorithm uses an adaptive crossover rate dynamically tuned during the evolutionary process. Finally, it is validated on a set of benchmark test functions for global numerical optimization. Compared with several existing algorithms, the performance of GNO2DE is superior to or not worse than that of these algorithms in terms of final accuracy, convergence speed, and robustness. In addition, we also especially compare the opposition-based DE algorithm with the DE algorithm without using the opposite point method, and the DE algorithm using “DE/rand/1/bin” or “DE/current to best/2/bin”, respectively.
文摘An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution algorithm. The algorithm adopts a nonlinear convergence strategy, a crossover strategy of differential evolution and the introduction of an elimination mechanism, which balances the global search and local exploitation ability of the algorithm and improves the accuracy of the solved optimal solution. The 13-unit and 40-unit systems are selected for economic load distribution calculation, and the experimental results show that the proposed improved algorithm is superior in distributing the economic load of the power system and can effectively reduce the economic cost.
基金Project(2011FJ3016)supported by the Research Foundation of Science & Technology Office of Hunan Province,China
文摘A novel and simple technique to control the search direction of the differential mutation was proposed.In order to verify the performance of this method,ten widely used benchmark functions were chosen and the results were compared with the original differential evolution(DE)algorithm.Experimental results indicate that the search direction controlled DE algorithm obtains better results than the original DE algorithm in term of the solution quality and convergence rate.
基金funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.
文摘This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.
基金Project (No. 2008AA06A413) supported by the National High-Tech R&D (863) Program of China
文摘The differential evolution(DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems(WDSs).This paper aims to carry out a comprehensive performance comparison between the new emerged DE algorithm and the most popular algorithm-the genetic algorithm(GA).A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454.A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison.It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study.Additionally,the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies,indicating that the DE exhibits comparable performance with other algorithms.It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs.
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.
基金supported by the Aeronautical Science Foundation of China (Nos.20151452021 and 20152752033)the National Natural Science Foundation of China (No.61300159)+1 种基金the Natural Science Foundation of Jiangsu Province of China (No.BK20130808)China Postdoctoral Science Foundation (No.2015M571751)
文摘Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization(IWO_DE/Ring) is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process(MOGP) is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.