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Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization 认领 引用 被引量：1
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作者 范勤勤 王循华 颜学峰 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第6期2227-2237,共11页
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat... A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application. 展开更多
关键词 harmony search differential evolution optimization co-evolution self-adaptive control parameter dynamic optimization
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Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms 认领 引用 被引量：3
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作者 Xu Chen Wenli Du Feng Qian 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2016年第11期1600-1608,共9页
Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-di... Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms. 展开更多
关键词 Dynamic optimization Differential evolution Ranking-based mutation operator Control vector parameterization
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Learning Convex Optimization Models 认领 引用 被引量：6
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作者 Akshay Agrawal Shane Barratt Stephen Boyd 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第8期1355-1364,共10页
A convex optimization model predicts an output from an input by solving a convex optimization problem.The class of convex optimization models is large,and includes as special cases many well-known models like linear a... A convex optimization model predicts an output from an input by solving a convex optimization problem.The class of convex optimization models is large,and includes as special cases many well-known models like linear and logistic regression.We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs,using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters.We describe three general classes of convex optimization models,maximum a posteriori(MAP)models,utility maximization models,and agent models,and present a numerical experiment for each. 展开更多
关键词 Convex optimization differentiable optimization machine learning
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Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm 认领 引用 被引量：1
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作者 Lili Tao Bin Xu +1 位作者 Zhihua Hu Weimin Zhong 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第8期983-991,共9页
The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemi... The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. Optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality. 展开更多
关键词 p-Xylene oxidation Operation condition optimization Multi-objective optimization Self-adaptive differential evolution
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Strip width spread prediction in rough rolling process based on mechanism modeling and optimization 认领 引用
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作者 Yan-jiu Zhong Jing-cheng Wang +1 位作者 Jia-hui Xu Jun Rao 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第12期2416-2424,共9页
Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian ... Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model,an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm(BADE)was proposed.At first,we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling temperature.Then,the BADE algorithm was proposed to optimize the improved spread mechanism model.The optimization algorithm is based on a novel adaptive differential evolution algorithm,which can effectively achieve the global optimal solution.Finally,the prediction performances of five machine learning algorithms were compared in experiments.The results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms,which proves the effectiveness of the proposed method. 展开更多
关键词 Hot rolling Width spread prediction Bayesian optimization:Adaptive differential evolution Parameter optimization
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Hybrid Meta-Model Based Design Space Differentiation Method for Expensive Problems 认领 引用 被引量：1
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作者 Nianfei Gan Guangyao Li Jichao Gu 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2016年第2期120-132,共13页
In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive p... In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance. 展开更多
关键词 hybrid meta-model design space differentiation expensive problems global optimization
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Dynamic reactive power planning method for CSP-PV hybrid power generation system 认领 引用
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作者 ZHANG Hong DONG Hai-ying +2 位作者 CHEN Zhao HUANG Rong DING Kun 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2020年第3期258-266,共9页
Aiming at the faults of some weak nodes in the concentrated solar power-photovoltaic(CSP-PV)hybrid power generation system,it is impossible to restore the transient voltage only relying on the reactive power regulatio... Aiming at the faults of some weak nodes in the concentrated solar power-photovoltaic(CSP-PV)hybrid power generation system,it is impossible to restore the transient voltage only relying on the reactive power regulation capability of the system itself.We propose a dynamic reactive power planning method suitable for CSP-PV hybrid power generation system.The method determines the installation node of the dynamic reactive power compensation device and its compensation capacity based on the reactive power adjustment capability of the system itself.The critical fault node is determined by the transient voltage stability recovery index,and the weak node of the system is initially determined.Based on this,the sensitivity index is used to determine the installation node of the dynamic reactive power compensation device.Dynamic reactive power planning optimization model is established with the lowest investment cost of dynamic reactive power compensation device and the improvement of system transient voltage stability.Furthermore,the component of the reactive power compensation node is optimized by particle swarm optimization based on differential evolution(DE-PSO).The simulation results of the example system show that compared with the dynamic position compensation device installation location optimization method,the proposed method can improve the transient voltage stability of the system under the same reactive power compensation cost. 展开更多
关键词 transient voltage recovery index sensitivity index dynamic reactive power planning optimization particle swarm optimization based on differential evolution(DE-PSO)
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HARDY-LITTLEWOOD-POLYA INEQUALITY FOR A LINEAR DIFFERENTIAL OPERATOR AND SOME RELATED OPTIMAL PROBLEMS 认领 引用
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作者 陈迪荣 孙永生 《Analysis in Theory and Applications》 1992年第1期50-58,共9页
In this paper a generalized version of the classical Hardy-Littlewood-Polya inequality is given.Furthermore,the Stechkin's problem for a linear differential operator is solved in L_2(R), and the optimal recovery p... In this paper a generalized version of the classical Hardy-Littlewood-Polya inequality is given.Furthermore,the Stechkin's problem for a linear differential operator is solved in L_2(R), and the optimal recovery problem for such differential operator is considered. 展开更多
关键词 Th HARDY-LITTLEWOOD-POLYA INEQUALITY FOR A LINEAR DIFFERENTIAL OPERATOR AND SOME RELATED OPTIMAL PROBLEMS
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Application of smoothing technique on twin support vector hypersphere 认领 引用 被引量：1
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作者 Wu Qing Gao Xiaofeng +1 位作者 Fan Jiulun Zhang Hengchang 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2020年第3期31-41,共11页
In order to improve the learning speed and reduce computational complexity of twin support vector hypersphere(TSVH),this paper presents a smoothed twin support vector hypersphere(STSVH)based on the smoothing technique... In order to improve the learning speed and reduce computational complexity of twin support vector hypersphere(TSVH),this paper presents a smoothed twin support vector hypersphere(STSVH)based on the smoothing technique.STSVH can generate two hyperspheres with each one covering as many samples as possible from the same class respectively.Additionally,STSVH only solves a pair of unconstraint differentiable quadratic programming problems(QPPs)rather than a pair of constraint dual QPPs which makes STSVH faster than the TSVH.By considering the differentiable characteristics of STSVH,a fast Newton-Armijo algorithm is used for solving STSVH.Numerical experiment results on normally distributed clustered datasets(NDC)as well as University of California Irvine(UCI)data sets indicate that the significant advantages of the proposed STSVH in terms of efficiency and generalization performance. 展开更多
关键词 twin support vector hypersphere Newton-Armijo algorithm smoothing approximation function unconstraint differentiable optimization
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Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines 认领 引用 被引量：7
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作者 Jun-hong ZHANG Yu LIU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第2期272-286,共15页
Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete en... Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines. 展开更多
关键词 Diesel Fault diagnosis Complete ensemble intrinsic time-scale decomposition （CE1TD） l,east square supportvector machine （LSSVM） Hybrid differential evolution and particle swarm optimization （HDEPSO）
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Pareto Efficiency of Finite Horizon Switched Linear Quadratic Differential Games 认领 引用 被引量：3
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作者 HUANG Yabing ZHAO Jun 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2018年第1期173-187,共15页
A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is e... A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is extended to dynamics switching situations to characterize the solutions of this multi-objective problem. Furthermore, the switched differential game is equivalently transformed into a family of parameterized single-objective optimal problems by introducing preference information and auxiliary variables. This transformation reduces the computing complexity such that the Pareto frontier of the switched LQ differential game can be constructed by dynamic programming. Finally, a numerical example is provided to illustrate the effectiveness. 展开更多
关键词 Dynamic optimization linear quadratic problems Pareto efficiency switched differential game
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