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Sequential quadratic programming enhanced backtracking search algorithm

Frontiers of Computer Science 2018年第2期12卷 316-330页

作者： Wenting ZHAO Lijin WANG Yilong YIN Bingqing WANG Yuchun TANG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China Research Center for Sectional and Imaging Anatomy Shandong University School of Medicine Jinan 250012 China

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

关键词： numerical optimization backtracking search algorithm sequential quadratic programming local search

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Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models

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APPLIED ENERGY 2018年 226卷 408-422页

作者： Yu, Kunjie Liang, J. J. Qu, B. Y. Cheng, Zhiping Wang, Heshan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Henan Peoples R China

Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.

关键词： Parameter identification Photovoltaic model backtracking search algorithm Multiple learning

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Application of backtracking search algorithm in load frequency control of multi-area interconnected power system

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AIN SHAMS ENGINEERING JOURNAL 2018年第2期9卷 257-276页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Jalpaiguri Govt Engn Coll Dept Elect Engn Jalpaiguri W Bengal India NIT Durgapur Dept Elect Engn Durgapur W Bengal India

This paper introduces a new powerful evolutionary algorithm called backtracking search algorithm (BSA) for solving load frequency control (LFC) problem in power system. Initially, two-area non-reheat thermal power plant is considered and gains of PI/PID controllers are optimized using BSA. This paper compares BSA's effectiveness in solving LFC problem with the performances of other optimization techniques reported in the literature. Nonlinearities of power system such as reheater, governor dead band, boiler dynamics and generation rate constraint are included in the system modeling to identify the system stability and its performance is compared with craziness based PSO technique. Additionally, two more test systems namely three-area and four-area hydro-thermal plant with nonlinearity are considered to demonstrate the efficiency of proposed algorithm. The comparative analysis of the performances indicates that the proposed controller gives better results than other techniques available in the literature. Sensitivity analysis showed robustness of proposed controller under loading and parameter uncertainty. (C) 2016 Ain Shams University. Production and hosting by Elsevier B.V.

关键词： Load frequency control backtracking search algorithm Boiler dynamics Governor dead band Sensitivity analysis Transient analysis

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Optimization of fed-batch fermentation processes using the backtracking search algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 91卷 286-297页

作者： Zain, Mohamad Zihin bin Mohd Kanesan, Jeevan Kendall, Graham Chuah, Joon Huang Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely backtracking search algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, MA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Fed-batch fermentation backtracking search algorithm Evolutionary algorithms Wastewater treatment Feeding trajectory optimization Sewage sludge

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Neural Network Approach for Estimating State of Charge of lithium-Ion Battery Using backtracking search algorithm

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IEEE ACCESS 2018年 6卷 10069-10079页

作者： Hannan, Mahammad A. Lipu, Molla S. Hossain Hussain, Aini Saad, Mohamad H. Ayob, Afida Univ Tenaga Nas Kajang 43000 Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43600 Malaysia

The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

关键词： Lithium-ion battery the state of charge back propagation neural network backtracking search algorithm electric vehicle

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A New SEBAL Approach Modified with backtracking search algorithm for Actual Evapotranspiration Mapping and On-Site Application

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第8期46卷 1213-1222页

作者： Atasever, Umit Haluk Ozkan, Coskun Erciyes Univ Engn Fac Dept Geomat Engn TR-38039 Kayseri Turkey

Actual evapotranspiration is one of the most important component for efficient water management and planning. Until recently, evapotranspiration computations and measurements have been performed locally. But, in recent years, actual evapotranspiration computations can be calculated regionally thanks to improvements on remote sensing discipline and satellites. Surface Energy Balance algorithm for Land (SEBAL) is one of the most commonly preferred technics for actual evapotranspiration mapping. However, this algorithm has some difficulties such as mismatch with Landsat 8 and hot-wet pixel selection which require expert knowledge. In this paper, a novel SEBAL based approach (SEBAL-BSA), which can use Landsat 8 images as data and can automatically perform hot-wet pixel selection using backtracking search algorithm (BSA) with ground control points, has been proposed. SEBAL-BSA was developed and tested using January 22-2015, April 28-2015 and July 01-2015 dated Landsat 8 images in Akarsu Irrigation Water User Association command area in the cukurova Region of Turkey and actual evapotranspiration mapping was realized without albedo measurements. Accuracy of SEBAL-BSA has been examined by comparing values of predefined ground truth points and values obtained from proposed approach. According to results of parametric matched pairs T test and nonparametric Wilcoxon sign rank test, SEBAL-BSA is highly successful. Applications also show that the SEBAL-BSA is a user-friendly approach for institutions and organizations related to water authorization and management.

关键词： Actual evapotranspiration SEBAL-BSA backtracking search algorithm Albedo

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An efficient method for parameter estimation of a nonlinear system using backtracking search algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2018年第3期21卷 338-350页

作者： Mishra, Puneet Kumar, Vineet Rana, K. P. S. Birla Inst Technol & Sci Dept Elect & Elect Engn Pilani Rajasthan India Netaji Subhas Inst Technol Div Instrumentat & Control Engn New Delhi 110078 India

Tank level systems are ubiquitous in process industries and exhibit a nonlinear nature. This nonlinear behaviour arises prominently due to nonlinear dependence of outflow rate on tank level and/or due to presence of other nonlinear elements in loop such as nonlinear actuators. It is essential to completely investigate the dynamics of system so as to generate an effective and accurate process model for obvious reasons. It may be noted that it is hard to find a unified model of a nonlinear system which can characterize the process in the entire operating range. To address this issue, this paper is a sincere effort to accurately identify a nonlinear tank level system with the help of a recently developed evolutionary algorithm i.e. backtracking search algorithm (BSA). Two different optimization problems were created and solved using BSA to estimate the tank level system parameters and are termed as Method 1 and Method 2, respectively. The optimization was performed to minimize summation of absolute error (SAE) between experimental and simulated data yielding the parameters of model. During the training phase, Method 1 used only one data set and Method 2 used a combination of five different data sets of experiments at different operating conditions. From the results obtained through the experimentation on a hardware setup, it can be easily inferred that parameter estimation using Method 2 gives better identification than Method 1 by providing lower SAE values. (C) 2018 Karabuk University. Publishing services by Elsevier B.V.

关键词： Parameter estimation backtracking search algorithm Tank level system Nonlinear system Evolutionary algorithm

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Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and backtracking search algorithm

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ENERGIES 2019年第19期12卷 3651页

作者： Kaboli, S. Hr. Aghay Al Hinai, Amer Al-Badi, A. H. Charabi, Yassine Al Saifi, Abdulrahim Sultan Qaboos Univ Sustainable Energy Res Ctr Muscat 123 Oman Sultan Qaboos Univ Dept Elect & Comp Engn Coll Engn Muscat 123 Oman Sultan Qaboos Univ Ctr Environm Studies & Res Muscat 123 Oman Petr Dev Oman PDO Muscat 100 Oman

The electromagnetic interference (EMI) generated by high voltage power systems can cause a serious problem for nearby electrically conductive structures, such as railroads, communication lines, or pipelines, that would place a system's integrity and the operational safety of the structure at high level of risk. According to the IEEE standard-80, by implementing a well-designed mitigation system, the induced voltage on neighboring electrically conductive structure can reach a harmless level. The mitigation system can enhance the overall integrity of pipelines and provide higher operation safety for personal during working on the exposed parts of metallic pipelines or conductive appurtenances. An accurate prediction about the level of induced voltage is absolutely necessary to design a suitable mitigation system for metallic pipelines. Thus, in this work a hybrid prediction methodology composed of an adaptive neuro-fuzzy inference system (ANFIS) and a backtracking search algorithm (BSA) is developed to accurately predict the electromagnetic inference's effects on metallic pipelines with shared right-of-way (RoW) and high voltage overhead lines (OHLs). Through the combination of BSA as a robust and efficient optimization algorithm in the learning process of an ANFIS approach, a hybrid data mining algorithm has been developed to predict the induced voltage on mitigated and unmitigated pipelines more accurately and reliably. The simulation results are validated by data sets observed from the Current Distribution, Electromagnetic Interference, Grounding and Soil Structure Analysis (CDEGS) software. From the simulation results it was confirmed that the proposed hybrid method is effective in accurately predicting the induced voltage on pipelines with changing system parameters. Furthermore, to evaluate the precision and applicability of the developed approach in this paper, its estimates are compared with the results obtained from an artificial neural network (ANN), a suppo

关键词： adaptive neuro-fuzzy inference system backtracking search algorithm electromagnetic interference overhead line pipeline

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Enhancing backtracking search algorithm using Reflection Mutation Strategy Based on Sine Cosine

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algorithmS 2019年第11期12卷 225-225页

作者： Zhou, Chong Li, Shengjie Zhang, Yuhe Chen, Zhikun Zhang, Cuijun Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Lab Artificial Intelligence & Machine Learning Shijiazhuang 050031 Hebei Peoples R China Beibu Gulf Univ Coll Resources & Environm Qinzhou 535011 Peoples R China

backtracking search algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.

关键词： evolutionary algorithm backtracking search algorithm reflection mutation sine cosine unit simplex

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Designing Lead-Lag PSS Employing backtracking search algorithm to Improve Power System Damping 9

Designing Lead-Lag PSS Employing Backtracking Search Algorit...

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9th IEEE-GCC Conference and Exhibition (GCCCE)

作者： Shafiullah, Md Rana, Md Juel Coelho, L. S. Abido, M. A. Al-Subhi, Ahmad King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil Univ Fed Parana Elect Engn Grad Program Dept Elect Engn Curitiba Parana Brazil

ISBN: (纸本)9781538627563

The power system is a complex network having highly nonlinear dynamics. Being electrically interconnected power systems are vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations (LFOs) originated from small disturbances in any part of the network may cause the outage of the whole system. Power System Stabilizer (PSS) with optimal parameter settings is an efficient tool for the suppression of these oscillations. In this paper, a novel approach is proposed to tune lead-lag controller based PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to suppress LFOs through improvement of power system damping. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

关键词： backtracking search algorithm Damping Ratio Eigenvalues Genetic algorithm Law-Frequency Oscillations Power System Stabilizer Single Machine Infinite Bus System

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