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Energy Consumption Forecasting for the Nonferrous Metallurgy Industry Using Hybrid Support Vector Regression with an Adaptive state transition algorithm

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COGNITIVE COMPUTATION 2020年第2期12卷 357-368页

作者： Huang, Zhaoke Yang, Chunhua Zhou, Xiaojun Yang, Shengxiang Cent South Univ Sch Automat Changsha 410083 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester Leics England

The nonferrous metallurgy industry is a major energy consumer in China, and accurate energy consumption forecasting for the nonferrous metallurgy industry can help government policymakers with energy planning. For this purpose, a hybrid support vector regression (HSVR) with an adaptive state transition algorithm (ASTA) named ASTA-HSVR is proposed to forecast energy consumption in the nonferrous metallurgy industry. The proposed support vector regression (SVR) model consists of a linear weighting of epsilon-SVR and nu-SVR. The ASTA was developed to optimize the parameters of the HSVR. Two cases of energy consumption from the nonferrous metallurgy industry in China are used to demonstrate the performance of the proposed method. The results indicate that the ASTA-HSVR method is superior to other methods. In this study, a hybrid support vector regression with an adaptive state transition algorithm (ASTA-HSVR) was developed and successfully applied to energy consumption forecasting for the nonferrous metallurgy industry. However, it should be noted that the outliers were not considered in this study. In the future, we expect to extend the ASTA-HSVR method to include energy consumption forecasting problems with outliers.

关键词： Energy consumption forecasting Support vector regression Adaptive state transition algorithm

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A Two-stage state transition algorithm for Constrained Engineering Optimization Problems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第2期16卷 522-534页

作者： Han, Jie Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

In this study, a state transition algorithm (STA) is investigated into constrained engineering design optimization problems. After an analysis of the advantages and disadvantages of two well-known constraint-handling techniques, penalty function method and feasibility preference method, a two-stage strategy is incorporated into STA, in which, the feasibility preference method is adopted in the early stage of an iteration process whilst it is changed to the penalty function method in the later stage. Then, the proposed STA is used to solve three benchmark problems in engineering design and an optimization problem in power-dispatching control system for the electro-chemical process of zinc. The experimental results have shown that the optimal solutions obtained by the proposed method are all superior to those by typical approaches in the literature in terms of both convergency and precision.

关键词： Constrained engineering optimization feasibility preference method penalty function method state transition algorithm

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Dynamic optimization based on state transition algorithm for copper removal process

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2827-2839页

作者： Huang, Miao Zhou, Xiaojun Huang, Tingwen Yang, Chunhua Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

The copper removal process (CRP) is an indispensable step for the purification of zinc sulfate solution by adding powdered zinc to a series of reactors in zinc hydrometallurgy. The selection of optimal amount of zinc powder is a complicated task because of the complex reaction mechanism, resulting in the fluctuation of copper ion concentration and the waste of zinc powder in the actual process. In this paper, we formulate a dynamic optimization problem (DOP) for the control of the zinc powder in CRP, aiming at reducing production costs and improving product quality simultaneously. A novel dynamic optimization method based on the state transition algorithm (STA) is investigated for solving this problem, and to improve the performance of STA, an adaptive strategy is adopted by its transformation operators. Simulation results from some classical DOPs show that the proposed method can optimize effectively and efficiently. The proposed approach is successfully applied to solve the DOP arising in CRP and the simulation results show that zinc powder consumption is considerably reduced under the assumption of an acceptable copper ion concentration.

关键词： Copper removal process Dynamic optimization state transition algorithm Global optimization

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Nonlinear system identification and control using state transition algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2014年 226卷 169-179页

作者： Zhou, Xiaojun Yang, Chunhua Gui, Weihua Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Univ Ballarat Sch Sci Informat Technol & Engn Ballarat Vic 3353 Australia

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Nonlinear system identification PID controller state transition algorithm Optimization algorithms

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A hierarchical evolution of neural architecture search method based on state transition algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第8期14卷 2723-2738页

作者： Du, Yangyi Zhou, Xiaojun Huang, Tingwen Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 10587 Qatar

Among different search strategies to realize neural architecture search (NAS), evolutionary algorithms (EAs) have gained much attention due to their global optimization capability. However, the large number of performance evaluation procedure makes the EA-based methods extremely time-consuming. To address this issue, an efficient framework called HENA (Hierarchical Evolution of Neural Architecture) is proposed in this paper. In HENA, NAS is hierarchically divided into two continuous phases, candidate operation search and connection relationship search. For this purpose, a supernet is defined to subsume the whole search space, where all weights can be inherited by all architectures. A novel evolutionary algorithm called state transition algorithm (STA) is used to traverse the search space continuously. In the first phase, several sampled architectures are trained on mini-group data to search better operation for different positions within the network. In the second phase, better connection relationship among operations can be directly determined without training via inheriting trained weights. Finally, the proposed method is evaluated on the widely used datasets, and the experimental results show that (1) the architecture learned by HENA obtains state-of-the-art performance (2.93% and 20.44% error rate on CIFAR-10 and CIFAR-100 respectively). (2) the learned architecture achieves 24.8% top-1 error on ImageNet dataset.

关键词： Deep neural networks Neural architecture search state transition algorithm Evolutionary algorithm

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A novel modularity-based discrete state transition algorithm for community detection in networks

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NEUROCOMPUTING 2019年 334卷 89-99页

作者： Zhou, Xiaojun Yang, Ke Xie, Yongfang Yang, Chunhua Huang, Tingwen Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

Complex network analysis is a hot topic in the data mining area which aims to reveal the hidden information behind a network. As an important tool in complex network analysis, community detection tries to perform a network clustering operation to find the community structure, which can be formulated as an optimization problem. In the past few decades, various of community detection algorithms have been designed to address this challenging problem. Although many algorithms are feasible to detect the network partitions, most of them only get suboptimal solutions or have poor stability. The state transition algorithm (STA) is a novel intelligent paradigm for global optimization, and it exhibits powerful global search ability in various complex optimization problems. Thus, in this paper, a novel modularity-based discrete state transition algorithm (MDSTA) is proposed to obtain more optimal and stable solutions. Moreover, based on the heuristic information of the network, vertex substitute transformation operator and community substitute transformation operator are proposed for global search. Then, each initialized individual evolves through these two substitute operations. Next, an elite population that contains individuals with high fitness values is selected from these evolved individuals. Finally, a two-way crossover operation among the elite population is conducted for local search. The framework of MDSTA is pretty simple and easy to implement. Several state-of-art community detection algorithms are used to compare with MDSTA both on artificial networks and real-world networks. The experimental results demonstrate that MDSTA is effective and stable for community detection in networks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： state transition algorithm Complex network Community detection Modularity

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A new multi-threshold image segmentation approach using state transition algorithm

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APPLIED MATHEMATICAL MODELLING 2017年 44卷 588-601页

作者： Han, Jie Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Thresholding plays an important role in image segmentation and image analysis. In this paper, the normalized histogram of an image is fitted by a linear combined normal distribution functions and each normal distribution function represents a class of pixels, whereas the parameters like the mean, the variance and the weights in the fitting function are undetermined. By transforming the fitting problem into a nonlinear and non-convex optimization problem, the state transition algorithm (STA) which is a new global optimization method is used to choose the optimal parameters of the fitting function. The effectiveness of proposed approach in multilevel thresholding problems is tested by several experimental results. By comparing with OTSU, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE) algorithm, it has shown that STA has competitive performance in terms of both optimization results and thresholding segmentation. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Image segmentation state transition algorithm Multilevel thresholding Global optimization

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A Hybrid Feature Selection Method Based on Binary state transition algorithm and ReliefF

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2019年第5期23卷 1888-1898页

作者： Huang, Zhaoke Yang, Chunhua Zhou, Xiaojun Huang, Tingwen Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

Feature selection problems often appear in the application of data mining, which have been difficult to handle due to the NP-hard property of these problems. In this study, a simple but efficient hybrid feature selection method is proposed based on binary state transition algorithm and ReliefF, called ReliefF-BSTA. This method contains two phases: the filter phase and the wrapper phase. There are three aspects of advantages in this method. First, an initialization approach based on feature ranking is designed to make sure that the initial solution is not easy to get tapped into local optimum. Then, a probability substitute operator based on feature weights is developed to update the current solution according to the different mutation probabilities of the features. Finally, a new selection strategy based on relative dominance is presented to find the current best solution. The simple and efficient algorithm k-nearest neighborhood with the leave-one-out cross validation is used as a classifier to evaluate feature subset candidates. The experimental results indicate that the proposed method is more efficient in terms of the classification accuracy through a comparison to other feature selection methods using seven public datasets and several real biomedical datasets. For public datasets, the proposed method improved the classification average accuracy by about 2.5% compared with the filter method. For a specific biomedical dataset AID1284, the classification accuracy significantly increased from 77.24% to 85.25% by using the proposed method.

关键词： Feature selection hybrid method state transition algorithm reliefF

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An Adaptive Dynamic Migration-based Constrained Multiobjective state transition algorithm 39

An Adaptive Dynamic Migration-based Constrained Multiobjecti...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Chen, Xiaolong Han, Jie Zhu, Liang Yang, Chunhua Wang, Xiaoli Cent South Univ Sch Automat Changsha Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Complex constrained multi-objective optimization problems are often characterized by narrow and disconnected feasible regions, making it challenging to obtain the optimal constrained Pareto front. To improve feasibility, diversity, and convergence in the optimization process, this paper introduces an adaptive dynamic migration-based constrained multiobjective state transition algorithm. The approach starts with an individual-based state transition algorithm, which searches for candidate solutions across the entire search space. Subsequently, these candidate solutions are evaluated using an adaptive dynamic migration constraint handling technique, which enhances the convergence of candidate solutions in the early stages of optimization and ensures their feasibility in the later stages. The migration process is adaptively adjusted based on the diversity and convergence of the population to maintain a balance among feasibility, diversity, and convergence. Experiments conducted on 14 benchmark problems reveal that the proposed method demonstrates superior or, at the very least, competitive performance when compared to other well-established methods.

关键词： Constrained multiobjective optimization constraint handling techniques state transition algorithm adaptive dynamic migration

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A matlab toolbox for continuous state transition algorithm 35

A matlab toolbox for continuous state transition algorithm

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35th Chinese Control Conference (CCC)

作者： Zhou, Xiaojun Yang, Chunhua Gui, Weihua Cenral South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9789881563910

state transition algorithm (STA) has been emerging as a novel stochastic method for global optimization in recent few years. To make better understanding of continuous STA, a matlab toolbox for continuous STA has been developed. Firstly, the basic principles of continuous STA are briefly described. Then, a matlab implementation of the standard continuous STA is explained, with several instances given to show how to use to the matlab toolbox to minimize an optimization problem with bound constraints. In the same while, a link is provided to download the matlab toolbox via available resources.

关键词： state transition algorithm matlab toolbox global optimization continuous optimization

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