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state-transition-algorithm-based resolution for overlapping linear sweep voltammetric peaks with high signal ratio

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 151卷 61-70页

作者： Wang, Guowei Yang, Chunhua Zhu, Hongqiu Li, Yonggang Peng, Xiongwei Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Cent S Univ Sch Met & Environm Changsha 410083 Hunan Peoples R China

To resolve the overlapping linear sweep voltammetric peaks (LSVPs) in the case of small signals overlapping to a very big one, a parameter optimization method based on state-transition-algorithm (STA) is investigated. First, four special state transformation operators of STA are introduced and a parameter optimization method is proposed. Then, the overlapping LSVPs are obtained by simultaneously determining trace amounts of Cd2+ and Co2+ in the presence of a high concentration of Zn2+ based on optimized reagents. Finally, overlapping LSVPs are resolved into independent sub-peaks using the proposed method. The resolution results show that the goodness-of-fit of fitting curve in describing the overlapping LSVPs is more than 97%. It indicates that the proposed method is reasonable and effective for the resolution of overlapping LSVPs in the case of high signal ratio which is more than 50. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Resolution of overlapping LSVPs High signal ratio Parameter optimization state transition algorithm Simultaneous determination

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Network Community Detection using A Backtracking-based Discrete state transition algorithm

Network Community Detection using A Backtracking-based Discr...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Ke Yang Xiaojun Zhou Chaojie Li School of Automation Central South University Changsha China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent years, a new evolutionary algorithm called state transition algorithm (STA) was created and developed. In our previous work, a population-based discrete STA (MDSTA) has been put forwarded to settle network community detection task. Similar to most population-based evolutionary algorithms, MDSTA has a relatively complex algorithm structure which may limit the application of the algorithm. To address this problem, a backtracking-based discrete STA (BDSTA) is designed in this study. BDSTA is an individual-based method, and two kinds of substitute operators based on label-based representation strategy and locus-based representation strategy are used in BDSTA for global search and local search, respectively. Owing to that the individual-based algorithms often fall into a stagnation solution, we employ a backtracking search strategy in the global search procedure. Finally, five real-world networks and the extended GN artificial networks are used to test BDSTA and some state-of-art algorithms. Experimental results prove that BDSTA often get high-quality community partitions and it is more efficient than these state-of-art algorithms.

关键词： state transition algorithm backtracking strategy complex network community detection Network communities complex networks state Change algorithms stormwater treatment areas evolutionary algorithm algorithm structure

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A new prediction model based on the leaching rate kinetics in the alumina digestion process

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HYDROMETALLURGY 2016年 164卷 7-14页

作者： Xie, Yongfang Wei, Simi Wang, Xiaoli Xie, Sen Yang, Chunhua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The leaching rate of alumina in the alumina digestion process is usually obtained via off-line analysis with a long time delay, leading to delayed control of the process and creating ongoing problems, such as a low leaching rate and wasted energy. Therefore, prediction of the online leaching rate is highly important. Based on mechanistic analysis of the double stream digestion process and the digestion kinetics of diaspore, a kinetics model established at the laboratory scale was scaled up to an industrial process. The unknown model parameters were estimated from the industrial data using a state transition algorithm (STA), which is a new and effective optimization algorithm. An error compensation model based on the kernel extreme learning machine (KELM) was subsequently built, and a prediction model for the leaching rate of alumina was established by parallel connection of the kinetics model with the compensation model. The validation results show that the model can predict the leaching rate of alumina for 90% of the samples with relative errors within 2% compared with the actual industrial data. The developed model will be further evaluated for control in the corresponding industrial process. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Leaching rate prediction Reaction kinetics state transition algorithm Kernel extreme leaming machine Compensation model

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Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy

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HYDROMETALLURGY 2018年 178卷 60-76页

作者： Zhang, Fengxue Yang, Chunhua Zhou, Xiaojun Zhu, Hongqiu Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The copper removal process is the first stage of purification in zinc hydrometallurgy. Due to its dynamic characteristics and complex reaction mechanism, a robust and effective controller to maintain high quality and stability of the outlet-ion-concentration is in great need. In this paper, a fractional order fuzzy proportional integral derivative (FOFPID) controller based on fuzzy logic is proposed to meet this challenge. The proposed work is conducted through a combination of three novel interdependent efforts. First, controller design problem is transformed into a nonconvex optimization problem. Second, a novel method named state transition algorithm (STA) is employed to solve the aforementioned optimization problem. Furthermore, in order to evaluate the performance of the proposed control strategy, the response performance of the system is analyzed. Finally, further tests are carried out to evaluate the performance of FOFPID controller, where disturbances caused by the measurement, flow rate, and inlet-ion-concentration are all taken into account. The simulation results demonstrate the superiority of the FOFPID controller in copper removal process over the competing FOPID and manual control in the same application environment.

关键词： Copper removal process Disturbance rejection Fractional order calculus Fuzzy logic control PID control state transition algorithm

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Adaptive over-sampling method for classification with application to imbalanced datasets in aluminum electrolysis

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 7183-7199页

作者： Huang, Zhaoke Yang, Chunhua Chen, Xiaofang Huang, Keke Xie, Yongfang Cent South Univ Sch Automat Changsha 410083 Peoples R China

The class imbalance problem often appears in practical applications, where one class has numerous instances and the other has only a few instances. Synthetic Minority Over-sampling TEchnique (SMOTE) is the most popular and commonly used sampling method to solve this problem. It has two important parameters: over-sampling rate N and number of nearest neighbors k. However, the two parameters that are arbitrarily chosen by users are not optimal in practical applications. In addition, the imbalance ratios of these datasets are absolutely different, which makes parameter selection in SMOTE more difficult. To overcome the problem, an adaptive over-sampling method is proposed in this study based on SMOTE. It transforms the parameter selection problem in SMOTE to a multi-objective optimization problem. Then, a new selection strategy named absolute dominance-based selection is proposed to obtain the current optimal solution. Finally, the state transition algorithm is used to search the best parameter values of SMOTE to achieve the optimal objectives. Four imbalanced benchmark datasets and four class-imbalanced aluminum electrolysis datasets are used to verify the validity of the proposed method. In comparison with other methods, the proposed method has the advantage of good classification performance. Numerical results also show that the proposed method can successfully solve the class imbalance problem in aluminum electrolysis.

关键词： Class imbalance problem Multi-objective optimization state transition algorithm SMOTE Aluminum electrolysis

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Multi-stage intelligent operation optimization for a hydrocracking fractionation system with a multi-fractionator series-parallel structure

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2020年第11期98卷 2342-2359页

作者： Wang, Yalin Sun, Jiazhou Sun, Bei Xue, Yongfei Shang, Dandan Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China

A fractionation system is an essential unit in the hydrocracking process. Its optimal operation is challenging because of the complexity in the structure of the distillation tower and composition of the stream. In addition, the series-parallel structure between the distillation towers of different techniques aggravates the coupling and complexity of the hydrocracking fractionation system (HFS). This, in turn, increases the time complexity of the optimization problem. In this paper, a rigorous mechanism model of an actual HFS is first applied to describe the operating conditions of the HFS. Then, an improved state transition algorithm (STA) with a staged evaluation strategy is proposed to solve the above problem. To overcome problems caused by the series-parallel structure of HFS, the model is divided into multiple stages for evaluation by mechanism analysis. Furthermore, several typical convergence estimation criteria are introduced to reduce unnecessary model calculations. To solve time-consuming problems associated with HFS optimization, the adaptive change operator is used to improve the search function of the original algorithm and two performance criteria are presented to reduce the optimization time. The proposed algorithm is successfully applied to the operational parameter optimization problem of HFS with a multi-fractionator series-parallel structure. The experimental results indicated that the staged evaluation strategy improved the fast convergence probability of the HFS mechanism model and reduced unnecessary calculations, whereas the improved algorithm increased accuracy and significantly decreased optimization time.

关键词： staged evaluation strategy state transition algorithm time-consuming optimization

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Layered online data reconciliation strategy with multiple modes for industrial processes

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CONTROL ENGINEERING PRACTICE 2018年 77卷 63-72页

作者： Xie, Sen Yang, Chunhua Yuan, Xiaofeng Wang, Xiaoli Xie, Yongfang Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Data reconciliation is a crucial technique to improve the accuracy of the measured data in industrial processes. However, most traditional data reconciliation researches mainly focused on global modeling for single mode processes, but little attention was paid to multimode processes. In this paper, a layered online data reconciliation strategy based on Gaussian mixture model is proposed for complex industrial processes with multiple modes. In the proposed data reconciliation framework, Gaussian mixture model is first used to identify and partition different operating modes from process data. Then, layered data reconciliation models are established for each operating mode. In the online data reconciliation step for new data, it is reconciled with the trained reconciliation models from different modes and its posteriors corresponding to different modes are calculated for new data. Finally, the reconciled result is obtained by the weighted sum of individual reconciled data in each operating mode. The effectiveness and feasibility of the proposed data reconciliation strategy are validated through a real industrial application on the sodium aluminate solution evaporation process.

关键词： Multimode processes Online data reconciliation Gaussian mixture model Data redundancy state transition algorithm

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Defect detection of nickel plated punched steel strip based on improved least square method

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OPTIK 2020年 206卷 164331-000页

作者： Cao, Binfang Li, Jianqi Liu, Chengfa Qin, Lingjie Hunan Univ Arts & Sci Hunan Prov Cooperat Innovat Ctr Construct & Dev D Changde Peoples R China Hunan Univ Arts & Sci Coll Math & Phys Sci Changde Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha Peoples R China

In order to solve the problems of false detection and missed detection on punched steel strip brought by manual inspection, a machine vision detection system for nickel plated punched steel strip is built, from which a defect detection method of nickel-plated punched steel strip based on improved least square method is presented. At first, the image is extracted and preprocessed in order to obtain clear edge images. Then state transition algorithm and iterative algorithm are used to improve least square method. The iterative algorithm is used to obtain the data set of the center coordinates and the radius of the fitted circle. While the state transition algorithm is used to perform the optimization of the results after multiple iterations to obtain the optimal solution of the center and radius parameters. Finally, the parameters such as hole diameter, transverse hole distance and longitudinal hole distance are calculated and used to realize the defect detection. The experimental results show that the method proposed can realize the parameters calculation with an absolute error of less than 10um. It also can realize defect detection of the blind hole and connecting hole for nickel plated punched steel strip.

关键词： Nickel plated punched steel strip Defect detection Circle fitting Least square method (LSM) state transition algorithm

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A multi-agent-based microgrid day-ahead optimal operation framework with liquid air energy storage by hybrid IGDT-STA

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JOURNAL OF ENERGY STORAGE 2024年 86卷

作者： Yao, Ruiqiu Xie, Hao Wang, Chunsheng Xu, Xiandong Du, Dajun Varga, Liz Hu, Yukun UCL Dept Civil Environm & Geomat Engn London WC1E 6BT England Cent South Univ Sch Automat Changsha 410083 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

Liquid air energy storage (LAES) is a promising energy storage technology for net-zero transition. Regarding microgrids that utilize LAES, the price of electricity in the market can create significant uncertainty within the system. To address this issue, the information gap decision theory (IGDT) method has proven to be an effective tool for resolving uncertainties in system operation. The IGDT method is a decision-making tool designed to tackle uncertainty, which can significantly enhance decision-making abilities in situations where information is scarce. Additionally, the state transition algorithm (STA) is a highly intelligent optimization algorithm that leverages structural learning. This study proposed a novel IGDT-STA hybrid method to solve the optimal operation of a microgrid with LAES while considering the uncertainty of market electricity prices. The IGDT-STA offers two distinct strategies for decision-makers who are either risk-averse or risk-taking. These strategies are subsequently optimized by the STA method. In addition, the IGDT-STA is implemented within a multi-agent framework to enhance system flexibility. Through a case study, it was found that the IGDT-STA employed good performance compared with the IGDT-genetic algorithm, stochastic method, and Monte Carlo method.

关键词： Optimal operation method Liquid air energy storage Information gap decision theory state transition algorithm Multi -agent system

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A Multi-Strategy-Improved Northern Goshawk Optimization algorithm for Global Optimization and Engineering Design

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Computers, Materials & Continua 2024年第7期80卷 1677-1709页

作者： Liang Zeng Mai Hu Chenning Zhang Quan Yuan Shanshan Wang School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China

Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering *** enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)*** algorithm incorporates a multifaceted enhancement strategy to boost operational ***,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible ***,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local *** is followed by the integration of T-distributionmutation strategies and the state transition algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and *** research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the *** experimental outcomes demonstrate INGO’s superior achievements in function optimization ***,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.

关键词： Northern Goshawk Optimization tent chaotic map T-distribution disturbance state transition algorithm UAV path planning

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