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Optimizing SVM's parameters based on backtracking search optimization algorithm for gear fault diagnosis

JOURNAL OF VIBROENGINEERING 2019年第1期21卷 66-81页

作者： Vantrong Thai Cheng, Junsheng Viethung Nguyen Phuonganh Daothi Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha Hunan Peoples R China Hanoi Univ Ind Fac Mech Engn Hanoi Vietnam Univ Econ & Tech Ind Fac Informat Technol Hanoi Vietnam

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for the SVM. The backtracking search optimization algorithm (BSA) is often applied to resolve the global optimization problem and adapted to optimize SVM parameters. In this research, a SVM parameter optimization method based on BSA (BSA-SVM) is proposed, and the BSA-SVM is applied to diagnose gear faults. Firstly, a gear vibration signal can be decomposed into several intrinsic scale components (ISCs) by means of the Local Characteristics-Scale Decomposition (LCD). Secondly, the MPE can extract the fault feature vectors from the first few ISCs. Thirdly, the fault feature vectors are taken as the input vectors of the BSA-SVM classifier. The analysis results of BSA-SVM classifier show that this method has higher accuracy than GA (Genetic algorithm) or PSO (Particles Swarm algorithm) algorithms combined with SVM. In short, the BSA-SVM based on the MPE-LCD is suitable to diagnose the state of health gear.

关键词： signal processing fault detection gears artificial neural networks backtracking search optimization algorithm

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Optimal Sizing of Islanded Microgrid using the backtracking search optimization algorithm with Standard Brownian Walk 10

Optimal Sizing of Islanded Microgrid using the Backtracking ...

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10th IEEE International Conference on Power Electronics, Drives and Energy Systems (IEEE PEDES)

作者： Abongmbo, Simon Benhaddou, Driss Krishnamoorthy, Harish Fan, Lei Shi, Jian Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Alfaisal Univ Coll Engn Riyadh Saudi Arabia Univ Houston Engn Technol Houston TX USA

ISBN: (纸本)9781665455664

In this paper, the backtracking search optimization (BSA) algorithm is developed for the optimal sizing of an island microgrid consisting of a wind energy system, photovoltaic (PV) system, diesel generator, and battery energy storage system (BESS). The objective is to determine the optimal sizes and combinations of the various available power production and storage component technologies such that the total annual investment and operation cost is minimized and all system-related resiliency and reliability limitations are satisfied. To optimize the utilization of renewable sources, a renewable fraction restriction is applied, and the total problem is formulated as a constrained optimization problem with the sizes of the power generation and storage components serving as the decision variables. The suggested rules-based Energy Management algorithm (EMA) controls the coordination of the various energy sources. The implementation is performed in MATLAB, and the results are utilized to evaluate the proposed methodology. The results show that converges faster than the classic Genetic algorithm (GA) by about 51%, and is more stable to perturbations of initial control parameters.

关键词： Annual Investment and Operation Cost backtracking search optimization algorithm Energy Management algorithm Islanded Microgrid Optimal sizing system reliability

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Development of backtracking search optimization algorithm Toolkit in LabVIEW™ 3

Development of Backtracking Search Optimization Algorithm To...

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3rd International Conference on Recent Trends in Computing (ICRTC)

作者： Sheoran, Yashpal Kumar, Vineet Rana, K. P. S. Mishra, Puneet Kumar, Jitendra Nair, Sreejith S. Netaji Subhash Inst Technol Div Instrumentat & Control Engn Sect 3 New Delhi 110078 India

In this paper a backtracking search optimization algorithm (BSA) toolkit has been developed in the LabVIEW (TM) environment. LabVIEW (TM) provides a graphical programming environment to design measurement and control applications. The development of BSA toolkit was motivated by the fact that only Differential Evolution (DE) toolkit was provided in LabVIEW (TM). Thus to design BSA toolkit, several modular virtual instruments have been developed for each BSA process. Developed BSA toolkit has been tested on several benchmark test functions and a comparative study with inbuilt DE toolkit has been performed, which shows results obtained from BSA toolkit are found superior to DE toolkit. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： backtracking search optimization algorithm optimization optimization Techniques Evolutionary algorithms Virtual Instruments

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Hybrid backtracking search optimization algorithm and K-Means for Clustering in Wireless Sensor Networks

Hybrid Backtracking Search Optimization Algorithm and K-Mean...

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IEEE DASC/PICom/DataCom/CyberSciTech Conferences

作者： Latiff, N. M. Abdul Malik, N. N. Nik Abdul Idoumghar, L. Univ Teknol Malaysia Fac Elect Engn Utm Johor Bahru 81310 Johor Malaysia Univ Haute Alsace LMIA Lab EA 3993 Mulhouse France

ISBN: (纸本)9781509040650

Rapid technology evolvement in the area of wireless sensor networks (WSNs) has led to many application-specific protocols that are particularly developed to cover different fields of usage and various network scenarios. Energy efficiency is one of the apparent challenges facing WSNs which has impacted immensely on the network performance. Hence, clustering protocols that eliminate energy inefficiencies in the network is essential. As finding an optimal set of cluster heads is an NP-hard problem, the application of heuristic algorithm is required to produce good clustering. In this paper, we propose a clustering solution for WSNs using a hybrid algorithm based on backtracking search optimization algorithm (BSA) and K-Means. A fitness function that incorporates aspects such as expected energy consumption in the network and maximum intracluster distance is utilized to address the problem of energy efficiency. Performance comparison against well-known clustering protocols such as LEACH and LEACH-C reveals that the hybrid of BSA and K-Means clustering algorithm is able to deliver more data to the base station and extends the network lifetime.

关键词： clustering energy efficient backtracking search optimization algorithm

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Wire Fault Diagnosis Using Time-Domain Reflectometry and backtracking search optimization algorithm 31

Wire Fault Diagnosis Using Time-Domain Reflectometry and Bac...

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31st International Review of Progress in Applied Computational Electromagnetics

作者： Boudjefdjouf, Hamza Bouchekara, Houssem R. E. H. Mehasni, Rabia Smail, Mostafa K. Orlandi, Antonio de Paulis, Francesco Univ Constantine 1 Dept Elect Engn Constantine 25000 Algeria Univ Paris Est IFSTTAR F-77747 Champs Sur Marne France Dept Ind & Informat Engn & Econ I-67100 Laquila Italy

ISBN: (纸本)9780996007818

The development of a robust and accurate fault diagnosis technique under various system and fault conditions is a very important research area. This paper describes a non-destructive method for wiring diagnosis that is based on time domain reflectometry in one hand and on backtracking search optimization algorithm in the other hand.

关键词： backtracking search optimization algorithm Time domain reflectometry Wiring diagnosis

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AGC of Practical Power System Using backtracking search optimization algorithm 9

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

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9th International Conference and Exposition on Electrical and Power Engineering (EPE)

作者： Pain, Santigopal Acharjee, Parimal Dr BC Roy Engn Coll Dept Appl Elect & Instrumentat Engn Durgapur India Natl Inst Technol Durgapur Dept Elect Engn Durgapur India

ISBN: (纸本)9781509061280

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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Quaternion-Based backtracking search optimization algorithm

Quaternion-Based Backtracking Search Optimization Algorithm

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Passos, Leandro Aparecido Rodrigues, Douglas Papa, Joao Paulo UFSCar Fed Univ Sao Carlos Dept Comp Sao Carlos SP Brazil UNESP Sao Paulo State Univ Sch Sci Bauru SP Brazil

ISBN: (纸本)9781728121536

Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based backtracking search optimization algorithm, a variant of the standard backtracking search optimization algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well.

关键词： backtracking search optimization algorithm Quaternions Meta-heuristics

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Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects

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ENERGY 2022年第0期248卷 123558-123558页

作者： Hu, Zhongbo Dai, Canyun Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter 'mixrate' value according to the current iteration number, is presented. To handle the system constraints, a 'repair thorn penalty' constraints handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness.

关键词： backtracking search optimization algorithm Dynamic economic dispatch Valve-point effects

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An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects

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ENERGY 2022年第PartE期239卷 122461-122461页

作者： Dai, Canyun Hu, Zhongbo Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Dynamic economic dispatch with valve-point effects (DED_vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： backtracking search optimization algorithm Dynamic economic dispatch Robustness Solution accuracy Valve-point effects

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Development of backtracking search optimization algorithm Toolkit in LabVIEW™

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Procedia Computer Science 2015年 57卷 241-248页

作者： Yashpal Sheoran Vineet Kumar K.P.S. Rana Puneet Mishra Jitendra Kumar Sreejith S. Nair Division of Instrumentation and Control Engineering Netaji Subhash Institute of Technology Sector-3 DwarkaNew Delhi-110078 India

In this paper a backtracking search optimization algorithm (BSA) toolkit has been developed in the LabVIEW™ environment. LabVIEW™ provides a graphical programming environment to design measurement and control applications. The development of BSA toolkit was motivated by the fact that only Differential Evolution (DE) toolkit was provided in LabVIEW™. Thus to design BSA toolkit, several modular virtual instruments have been developed for each BSA process. Developed BSA toolkit has been tested on several benchmark test functions and a comparative study with inbuilt DE toolkit has been performed, which shows results obtained from BSA toolkit are found superior to DE toolkit.

关键词： backtracking search optimization algorithm optimization optimization Techniques Evolutionary algorithms Virtual Instruments.

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