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A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints

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Neurocomputing 2016年 186卷 182-194页

作者： Zikang Su Honglun Wang Peng Yao School of Automation Science and Electrical Engineering Beihang University 100191 Beijing China Honors College of Beihang University 100191 Beijing China The Science and Technology on Aircraft Control Laboratory Beihang University 100191 Beijing China

Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even with direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) with the hp-adaptive Gauss pseudo-spectral methods (hpGPM) is proposed. Firstly, BSA is improved to enhance its convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback. Then, at the beginning stage of the hybrid search process, an initialization generator is constructed using IBSA to find a near optimum solution. When the change in fitness function approaches to a predefined value which is small enough, the search process is replaced by hpGPM to accelerate the search process and find an accurate solution. By this way, the hybrid algorithm is able to find a global optimum more quickly and accurately. Two NOC problems with CDC are examined using the proposed algorithm, and the corresponding Monte Carlo simulations are conducted. The comparison results show the hybrid algorithm achieves better performance in convergent speed, accuracy and robustness.

关键词： backtracking search optimization algorithm Hp-adaptive gauss pseudospectral methods Harmony search algorithm Nonlinear optimal control

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AGC of Practical Power System Using backtracking search optimization algorithm

AGC of Practical Power System Using Backtracking Search Opti...

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International Conference and Exposition on Electrical and Power Engineering

作者： Santigopal Pain Parimal Acharjee Department of Applied Electronics and Instrumentation Engineering Dr. B. C. Roy Engineering College Department of Electrical Engineering National Institute of Technology

ISBN: (纸本)9781509061303

In this paper, backtracking search optimization algorithm (BSA) is proposed to solve automatic generation control (AGC) problem to achieve the best steady state and transient performances of power system. A practical two area multisource power system model is developed by considering different generating units (non-reheat, reheat and hydro) with different real time system parameters and non-linear security constraints such as governor dead band (GDB), generation rate constraint (GRC) and time delay (TD). BSA is used to tune the proportional integral derivative (PID) control parameters to improve the AGC performances considering all practical limitations. A new and innovative cost function is formulated considering area control error, stead state error, maximum overshoot, undershoot and number of peaks present in the output waveform with proper weight factors. The performance of the proposed method is compared with real coded genetic algorithm (RCGA) and particle swarm optimization (PSO) algorithm. Simulation results validate the better performance of BSA tuned PID control method compare to other two methods.

关键词： backtracking search optimization algorithm Automatic generation control Non-linear security constraints Cost function

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Design of backtracking search optimization paradigm for joint amplitude-angle measurement of sources lying in fraunhofer zone

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MEASUREMENT 2020年 149卷 106977-000页

作者： Raja, Muhammad Asif Zahoor Akhtar, Rizwan Chaudhary, Naveed Ishtiaq Khan, Wasim Ullah Zhu Zhiyu Jamil, Abid Zaman, Fawad Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang Jiangsu Peoples R China COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Anhui Peoples R China

In this study, the strength of nature inspired computing (NIC) paradigm through backtracking search algorithms (BSA) is exploited for measurement of amplitude and direction of arrival (DOA) parameters of sources impinging on uniform linear array from Fraunhofer zone. The NIC techniques based on variant of BSAs are designed through varying the backtracking history and individual of the populations for effective optimization of the fitness evaluation function based on mean squared error between true and measured responses. optimization mechanism of BSAs is applied for different degrees of freedom models of DOA from far field sources for noisy and noiseless environment. Comparative studies are conducted through outcomes of statistics based on multiple executions of each solver by means of different gauges of accuracy and complexity. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Amplitude measurement Direction of arrival backtracking search optimization algorithm Parameter estimation

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An application of backtracking search optimization-based least squares support vector machine for prediction of short-term wind speed

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WIND ENGINEERING 2020年第3期44卷 266-281页

作者： Tian, Zhongda Ren, Yi Wang, Gang Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Peoples R China

Wind speed prediction is an important technology in the wind power field;however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization-based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm-based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R-2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7

关键词： backtracking search optimization algorithm least squares support vector machine short-term wind prediction optimization

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An approach towards efficient scheduling of home energy management system using backtracking search optimization and tabu search 32

An approach towards efficient scheduling of home energy mana...

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32nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Shafiq, Sundas Asif, Sikander Fatima, Iqra Yousaf, Kubra Safat, Wajiha Javaid, Nadeem COMSATS Inst Informat Technol Dept Comp Sci Islamabad 44000 Pakistan Bahria Univ Dept Software Engn Islamabad 44000 Pakistan

ISBN: (纸本)9781538653951

Smart grid is an emerging technology which is successfully implemented by the use of different communication methods. Demand side management (DSM) plays a significant role in the management of load and energy consumption in order to reduce cost in the smart grids. Smart buildings and smart homes are usually considered important for reducing the electricity consumption by home energy management controllers (HEMC). In this paper, an energy management controller (EMC) is proposed with an objective to minimize the energy consumption, curtail load demand, reduce electricity usage and minimize peak to average (PAR) ratio with increased user comfort level. For this purpose, three evolutionary and iterative techniques were proposed as backtracking search optimization algorithm (BSOA), tabu search (TS) and their hybrid as tabu backtracking search optimization (TBSO) algorithm. Simulations were performed in MATLAB by using real time pricing (RTP) tariff for bill calculation. 9 household appliances were scheduled and results proved that our proposed hybrid technique outperforms the remaining two in terms of cost minimization.

关键词： Residential area domain Smart gird backtracking search optimization algorithm Tabu search Demand side management

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An Improved Neural Network with Random Weights Using backtracking search algorithm

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NEURAL PROCESSING LETTERS 2016年第1期44卷 37-52页

作者： Wang, Bingqing Wang, Lijin Yin, Yilong Xu, Yunlong Zhao, Wenting Tang, Yuchun Shandong Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Shandong Univ Res Ctr Sect & Imaging Anat Sch Med Jinan 250012 Peoples R China

This paper proposes a hybrid algorithm by combining backtracking search algorithm (BSA) and a neural network with random weights (NNRWs), called BSA-NNRWs-N. BSA is utilized to optimize the hidden layer parameters of the single layer feed-forward network (SLFN) and NNRWs is used to derive the output layer weights. In addition, to avoid over-fitting on the validation set, a new cost function is proposed to replace the root mean square error (RMSE). In the new cost function, a constraint is added by considering RMSE on both training and validation sets. Experiments on classification and regression data sets show promising performance of the proposed BSA-NNRWs-N.

关键词： Neural network Random weights backtracking search optimization algorithm Cost function

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An improved hybrid backtracking search algorithm based T-S fuzzy model and its implementation to hydroelectric generating units

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NEUROCOMPUTING 2018年 275卷 2066-2079页

作者： Yan, Shuangqing Zhou, Jianzhong Zheng, Yang Li, Chaoshun Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

In order to realize the automatic selection of rule number in fuzzy space partition in the identification of the Takagi-Sugeno (T-S) fuzzy model, a new fuzzy clustering method is proposed in this paper. With the purpose of searching for the optimal fuzzy cluster number and the corresponding cluster centers simultaneously, a novel improved hybrid backtracking search algorithm (IHBSA) has been proposed by introducing the idea of a hybrid encoding scheme as well as a variable valid length chromosome to this optimization algorithm. With a proper cluster validity index taken as the objective function, IHBSA is capable of partitioning the fuzzy space and identifying premise parameters of the T-S fuzzy model without setting a deterministic value of cluster number as a priori. Through the experimental analysis, it is demonstrated that the proposed method possesses higher approximation accuracy with relatively fewer rule number in comparison with traditional ones. Moreover, the T-S fuzzy model is implemented to the practical data of hydroelectric generating units (HGU), and preponderant trajectory matching performance has also been achieved when utilizing the proposed method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fuzzy model identification T-S fuzzy model backtracking search optimization algorithm Hybrid encoding Fuzzy cluster validity index Hydroelectric generating units

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A new hybrid differential evolution algorithm with self-adaptation for function optimization

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APPLIED INTELLIGENCE 2018年第7期48卷 1657-1671页

作者： Nama, Sukanta Saha, Apu Kumar Natl Inst Technol Agartala Dept Math Jirania 799046 West Tripura India

In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and backtracking search optimization algorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.

关键词： Global optimization algorithm backtracking search optimization algorithm Differential evolution Hybrid algorithm CEC2013

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An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem

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ENERGY CONVERSION AND MANAGEMENT 2015年第Feb.期91卷 225-237页

作者： Yuan, Xiaohui Ji, Bin Yuan, Yanbin Ikram, Rana M. Zhang, Xiaopan Huang, Yuehua Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

This paper establishes a model to deal with the short-term hydrothermal generation scheduling (SHTGS) problem. The problem is composed of three interconnected parts: short-term hydrothermal coordination, thermal unit commitment and economic load dispatch. An efficient hybrid method composed of chaotic backtracking search optimization algorithm and binary charged system search algorithm (CBSA-BCSS) is proposed to solve this problem. In order to analyze the effect of the chaotic map on the performance of the method, three different chaotic maps are adopted to integrate into the proposed method and the corresponding consequences are achieved. Furthermore, efficient heuristic search strategies are adopted to handle with the complicated constraints of the SHTGS system. Finally, a hydrothermal unit commitment system is utilized to verify the feasibility and effectiveness of the proposed method. The results demonstrate the efficiency of the hybrid optimization method and the appropriation of the constraint handling strategies. The comparison of the solutions achieved by different methods shows that the proposed method has higher efficiency in terms of solving SHTGS problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal system backtracking search optimization algorithm Binary charged system search algorithm Chaotic mapping

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Novel Computational Heuristics for Wireless Parameters Estimation in Bistatic Radar systems

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 909-927页

作者： Zaman, Fawad Hassan, Ali Akbar, Sadiq Rehman, Ata Ur Raja, Muhammad Asif Zahoor COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock Pakistan Univ Peshawar Dept Elect Peshawar Pakistan

Research in the field of bistatic radars have growing interest with exclusive importance in defence sector, aerospace industry, remote sensing, meteorological and navigation applications. In this work, joint wireless parameters of electromagnetic plane waves impinging on the receiver of bistatic radar are estimated by exploiting the efficacy of bio-inspired heuristics through backtracking search optimization algorithm (BSA). The wireless parameters, the amplitude, elevation and azimuth angles, are estimated form the data received on 1-L and 2-L structured antenna arrays deployed on the receiver of bistatic radar. Mean square error is a performance metric incorporated for fitness evaluation, constructed on the basis of difference between the desired and actual responses of the system. Monte Carlo simulations of the meta-heuristic algorithm BSA are performed for 1-L as well as 2-L structured antenna arrays to validate and verify the worth of the scheme in terms of estimation accuracy, reliability, robustness and proximity effect. Comparative study of BSA with state of art counterparts further demonstrate its effectiveness.

关键词： Bistatic radar Parameter estimation Electromagnetic plane waves Antenna arrays backtracking search optimization algorithm

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