Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy comp...Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy components.The geometry of the weld bead(height and width)is influenced by multiple intricate parameters and variables during the manufacturing process.Accurately predicting the weld bead shape enables precise control over the surface flatness of the part,helping to prevent defects such as lack of fusion.This significantly reduces dimensional redundancy,enhances printing efficiency,and optimizes material usage.In this study,a quadratic regression prediction model for weld bead geometry was developed using the response surface methodology(RSM),with predictions generated through several machine learning models.These models included the back-propagation neural network(BPNN),support vector regression(SVR),multi-output support vector regression(MOSVR),extreme learning machine(ELM),and a differential evolution-optimized MOSVR(DE-MOSVR)model.Grid search and cross-validation techniques were utilized to identify the optimal parameters for each model to achieve the best predictive performance.A comparison of these models was conducted,followed by an evaluation of their generalization capabilities using an additional 20 sets of test data.The most accurate predictive model was selected based on a comprehensive assessment.The results showed that the DE-MOSVR model outperformed the others,achieving mean squared error,root mean squared error,mean absolute error,and R^(2) values for width(height)predictions of 0.0411(0.0041),0.2028(0.0639),0.1671(0.0550),and 0.9434(0.9433),respectively.It demonstrated the smallest deviation in the validation set,with mean deviations of 1.97% and 1.68%,respectively.The model we developed was validated through the production of prototype parts,providing valuable reference and guidance for predicting and modeling weld bead morphology in the Wire-fed LA-DED process.展开更多
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t...To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.展开更多
Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms ...Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.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 determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID co...To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.展开更多
Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compur...Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.展开更多
A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to...A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.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
Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinizat...Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.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
To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization(DEGSO) is proposed,which is b...To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization(DEGSO) is proposed,which is based on the differential evolution(DE) and the group search optimization(GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.展开更多
Aiming at the hybrid flow-shop （HFS） scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution （DE） is proposed. By using a...Aiming at the hybrid flow-shop （HFS） scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution （DE） is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.展开更多
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...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
In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evoluti...In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.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
This paper introduces a pneumatic finger cylinder servo control system for medical grabbing.First,according to the physical structure of the proportional directional valve and the pneumatic cylinder,the state equation...This paper introduces a pneumatic finger cylinder servo control system for medical grabbing.First,according to the physical structure of the proportional directional valve and the pneumatic cylinder,the state equation of the gas in the servo system was obtained.The Stribeck friction compensation model of a pneumatic finger cylinder controlled by a proportional valve was established and the experimental platform built.To allow the system output to bet-ter track the change in the input signal,the flow-gain compensation method was adopted.On this basis,a friction compensation control strategy based on a differential evolution algorithm was proposed and applied to the position control system of a pneumatic finger cylinder.Finally,the strategy was compared with the traditional proportional derivative(PD)strategy and that with friction compensation.The experimental results showed that the position accuracy of the finger cylinder position control system can be improved by using the friction compensation strategy based on the differential evolution algorithm to optimize the PD parameters.展开更多
In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineerin...In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.展开更多
A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neut...A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary calculations.Furthermore,the crossover probability and scaling factor are self-adaptively controlled.FLUKA Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor spectrum.Subsequently,the measured neutron counts are unfolded using the SDENUA.The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default spectrum.The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.展开更多
Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the r...Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the restored loads, switch frequency and generator efficiency are taken into account. In this model, analytic hierarchy process(AHP) is proposed to determine the coefficients of these objective functions. Meanwhile, a quantum differential evolution algorithm with triple quantum bit code is proposed. This algorithm aiming at the characteristics of shipboard power system is different from the normal quantum bit representation. The individual polymorphic expression is realized, and the convergence performance can be further enhanced in combination with the global parallel search capacity of differential evolution algorithm and the superposition properties of quantum theory. The local optimum can be avoided by dynamic rotation gate. The validity of algorithm and model is verified by the simulation examples.展开更多
An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the obj...An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.展开更多
基金supported by Natural Science Foundation of Shandong Province(Grant No.ZR202212010161)Natural Science Foundation of Qingdao(Grant No.23-2-1-83-zyyd-jch)+1 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515110116)the National Natural Science Foundation of China(Grant No.52405359).
文摘Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy components.The geometry of the weld bead(height and width)is influenced by multiple intricate parameters and variables during the manufacturing process.Accurately predicting the weld bead shape enables precise control over the surface flatness of the part,helping to prevent defects such as lack of fusion.This significantly reduces dimensional redundancy,enhances printing efficiency,and optimizes material usage.In this study,a quadratic regression prediction model for weld bead geometry was developed using the response surface methodology(RSM),with predictions generated through several machine learning models.These models included the back-propagation neural network(BPNN),support vector regression(SVR),multi-output support vector regression(MOSVR),extreme learning machine(ELM),and a differential evolution-optimized MOSVR(DE-MOSVR)model.Grid search and cross-validation techniques were utilized to identify the optimal parameters for each model to achieve the best predictive performance.A comparison of these models was conducted,followed by an evaluation of their generalization capabilities using an additional 20 sets of test data.The most accurate predictive model was selected based on a comprehensive assessment.The results showed that the DE-MOSVR model outperformed the others,achieving mean squared error,root mean squared error,mean absolute error,and R^(2) values for width(height)predictions of 0.0411(0.0041),0.2028(0.0639),0.1671(0.0550),and 0.9434(0.9433),respectively.It demonstrated the smallest deviation in the validation set,with mean deviations of 1.97% and 1.68%,respectively.The model we developed was validated through the production of prototype parts,providing valuable reference and guidance for predicting and modeling weld bead morphology in the Wire-fed LA-DED process.
文摘To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.
基金funded by National Special Project Number for International Cooperation under Grant 2015DFR11050the Applied Science and Technology Research and Development Special Fund Project of Guangdong Province under Grant 2016B010126004.
文摘Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.
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the National Natural Science Foundation of China (60375001)the Scientific Research Foundation of Hunan Provincial Education Department (05B016).
文摘To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.
基金Supported by the National Natural Science Foundation of China （60736021）, the National High Technology Research and Development Program of China （2006AA04Z184, 2007AA041406）, and the Key Technologies R＆D Program of Zhejiang Province （2006C 11066, 2006C31051）.
文摘Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.
基金Supported by the National Natural Science Foundation of China （20506003, 20776042） and the National High-Tech Research and Development Program of China （2007AA04Z 164）.
文摘A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.
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.
基金This work was supported by the National Natural Science Foundation of China(No.60375001)the High School Doctoral Foundation of China(NO.20030532004).
文摘Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.
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 the Major State Basic Research Development Program of China（2012CB720500）the National Natural Science Foundation of China（U1162202）,the National Natural Science Foundation of China（61174118）+2 种基金the National High Technology Research and Development Program of China（2012AA040307）Shanghai Key Technologies R＆D program（12dz1125100）the Shanghai Leading Academic Discipline Project（B504）
文摘To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization(DEGSO) is proposed,which is based on the differential evolution(DE) and the group search optimization(GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.
基金supported by the National Natural Science Fundation of China (60774082 70871065+2 种基金 60834004)the Program for New Century Excellent Talents in University (NCET-10-0505)the Doctoral Program Foundation of Institutions of Higher Education of China(20100002110014)
文摘Aiming at the hybrid flow-shop （HFS） scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution （DE） is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.
基金This work was 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Project(20040533035)supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject(60874070)supported by the National Natural Science Foundation of China
文摘In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.
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.
基金Supported by Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(Grant No.GZKF-202016)Henan Province Science and Technology Key Project of China(Grant No.202102210081)+1 种基金Fundamental Research Funds for Henan Province Colleges and Universities of China(Grant No.NSFRF140120)Doctor Foundation of Henan Polytechnic University of China(Grant No.B2012-101).
文摘This paper introduces a pneumatic finger cylinder servo control system for medical grabbing.First,according to the physical structure of the proportional directional valve and the pneumatic cylinder,the state equation of the gas in the servo system was obtained.The Stribeck friction compensation model of a pneumatic finger cylinder controlled by a proportional valve was established and the experimental platform built.To allow the system output to bet-ter track the change in the input signal,the flow-gain compensation method was adopted.On this basis,a friction compensation control strategy based on a differential evolution algorithm was proposed and applied to the position control system of a pneumatic finger cylinder.Finally,the strategy was compared with the traditional proportional derivative(PD)strategy and that with friction compensation.The experimental results showed that the position accuracy of the finger cylinder position control system can be improved by using the friction compensation strategy based on the differential evolution algorithm to optimize the PD parameters.
基金Supported by the National Basic Research Program of China (2012CB720500)the National Natural Science Foundation of China (60974008)
文摘In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.
基金supported by the National Key R&D Program of the MOST of China(No.2016YFA0300204)the National Natural Science Foundation of China(Nos.11227902)as part of the Si PáME2beamline project+1 种基金supported by the National Natural Science Foundation of China(No.41774120)the Sichuan Science and Technology Program(No.2021YJ0329)。
文摘A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary calculations.Furthermore,the crossover probability and scaling factor are self-adaptively controlled.FLUKA Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor spectrum.Subsequently,the measured neutron counts are unfolded using the SDENUA.The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default spectrum.The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.
基金the National Natural Science Foundation of China(No.51175321)the Innovation Program of Shanghai Municipal Education Commission(No.12ZZ158)
文摘Fault reconfiguration of shipboard power system is viewed as a typical nonlinear and multi-objective combinatorial optimization problem. A comprehensive reconfiguration model is presented in this paper, in which the restored loads, switch frequency and generator efficiency are taken into account. In this model, analytic hierarchy process(AHP) is proposed to determine the coefficients of these objective functions. Meanwhile, a quantum differential evolution algorithm with triple quantum bit code is proposed. This algorithm aiming at the characteristics of shipboard power system is different from the normal quantum bit representation. The individual polymorphic expression is realized, and the convergence performance can be further enhanced in combination with the global parallel search capacity of differential evolution algorithm and the superposition properties of quantum theory. The local optimum can be avoided by dynamic rotation gate. The validity of algorithm and model is verified by the simulation examples.
基金supported by the National Natural Science Foundation of China(6083500460775047+4 种基金60974048)the National High Technology Research and Development Program of China(863 Program)(2007AA0422442008AA04Z214)the Natural Science Foundation of Hunan Province(09JJ9012)Scientific Research Fund of Hunan Provincial Education Department(08C337)
文摘An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.
