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backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options

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ENERGY 2016年第Part1期116卷 637-649页

作者： Modiri-Delshad, Mostafa Kaboli, S. Hr Aghay Taslimi-Renani, Ehsan Abd Rahim, Nasrudin Wisma R&D Univ Malaya UM Power Energy Dedicated Adv Ctr UMPEDAC Level 4Jalan Pantai Baharu Kuala Lumpur 59990 Malaysia King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia

This paper presents backtracking search algorithm (BSA) for solving economic dispatch (ED) problems with considering valve-point loading effects, prohibited operating zones, and multiple fuel options. The proposed method is an evolutionary technique of optimization with simple structure and single control parameter to solve numerical optimization problems. It is a powerful method for effectively exploring the search space of an optimization problem to find the optimal solution within a low computation time. Different test systems with up to 160 generating units have been used to show the performance of BSA to solve ED problems with high nonlinearities. The results are compared with several methods of optimization to verify the high performance of BSA for solving the ED problems. Statistical analysis of the results among 50 independent runs has been carried out to validate the BSA as a highly robust method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Valve-point loading effects Multiple fuel option Economic dispatch Non-convex Transmission loss backtracking search algorithm

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backtracking search algorithm with three constraint handling methods for constrained optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第21期42卷 7831-7845页

作者： Zhang, Chunjiang Lin, Qun Gao, Liang Li, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

A new evolutionary algorithm, backtracking search algorithm (BSA), is applied to solve constrained optimization problems. Three constraint handling methods are combined with BSA for constrained optimization problems;namely feasibility and dominance (FAD) rules, epsilon-constrained method with fixed control way of epsilon value and a proposed epsilon-constrained method with self-adaptive control way of epsilon value. The proposed method controls epsilon value according to the properties of current population. This kind of epsilon value enables algorithm to sufficiently search boundaries between infeasible regions and feasible regions. It can avoid low search efficiency and premature convergence which happens in fixed control method and FAD rules. The comparison of the above three algorithms demonstrates BSA combined epsilon-constrained method with self-adaptive control way of epsilon value (BSA-SA epsilon) is the best one. The proposed BSA-SA epsilon also outperforms other five classic and the latest constrained optimization algorithms. Then, BSA-SA epsilon has been applied to four engineering optimization instances, and the comparison with other algorithms has proven its advantages. Finally, BSA-SA epsilon is used to solve the car side impact design optimization problem, which illustrates the wide application prospects of the proposed BSA-SA epsilon. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： backtracking search algorithm Constrained optimization problem Feasibility and dominance rules epsilon-constrained method Engineering optimization

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backtracking search algorithm driven by generalized mean position for numerical and industrial engineering problems

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 11985-12031页

作者： Zhang, Yiying Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

backtracking search algorithm (BSA) is a very popular and efficient population-based optimization technique. BSA has a very simple structure and good global search ability. However, BSA may be trapped into the local optimum in solving challenging multimodal optimization problems due to the single learning strategy. To enhance the global search ability of BSA, this paper proposes an improved version of BSA called backtracking search algorithm driven by generalized mean position (GMPBSA). In GMPBSA, two types of generalized mean positions are defined based on the built feature zones, which are employed to design the comprehensive learning mechanism consisting of three candidate learning strategies. Note that, this learning mechanism doesn't introduce new control parameters and refer to the complex calculation. To verify the performance of GMPBSA, GMPBSA is used to solve the well-known CEC 2013 and CEC 2017 test suites, and three complex engineering optimization problems. Experimental results support the great potential of GMPBSA applied to the challenging multimodal optimization problems. The source code of GMPBSA can be found from

关键词： backtracking search algorithm Generalized mean position Swarm intelligence Engineering design

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backtracking search algorithm for effective and efficient surface wave analysis

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JOURNAL OF APPLIED GEOPHYSICS 2015年 114卷 19-31页

作者： Song, Xianhai Zhang, Xueqiang Zhao, Sutao Li, Lei China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Hubei Peoples R China

Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on backtracking search algorithm (BSA), a novel and powerful evolutionary algorithm (EA). Development of BSA is motivated by studies that attempt to develop an algorithm that possesses desirable features for different optimization problems which include the ability to reach a problem's global minimum more quickly and successfully with a small number of control parameters and low computational cost, as well as robustness and ease of application to different problem models. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and effectiveness of BSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of BSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on real surface wave data. Furthermore, the performance of BSA is compared against that of GA by real data to further evaluate scores of BSA. Results from both synthetic and actual data demonstrate that BSA applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of BSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence Evolutionary algorithms backtracking search algorithm Surface waves Dispersion curves

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backtracking search algorithm with competitive learning for identification of unknown parameters of photovoltaic systems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 160卷 113750-113750页

作者： Zhang, Yiying Ma, Maode Jin, Zhigang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Metaheuristic algorithms have been successfully used to parameter identification of photovoltaic systems. However, this still faces the following two challenges. Firstly, most of the applied algorithms are complex and need some extra control parameters except the essential population size and stopping criterion, which is against their applications in photovoltaic systems with different characteristics. Secondly, how to obtain model parameters with higher accuracy and reliability has been a very valuable topic. To address the two challenges, this paper presents a new optimization method called backtracking search algorithm with competitive learning (CBSA) for parameter identification of photovoltaic systems. The remarkable features of CBSA are that it has a very simple structure and only needs the essential parameters. The core idea of CBSA is to increase the chance of backtracking search algorithm (BSA) to jump out of the local optimum by the designed competitive learning mechanism. In CBSA, the population is first divided into two subpopulations by built competitive mechanism. Then each subpopulation is optimized by the different search operators with multiple learning strategies. In order to test the performance of CBSA, CBSA is first employed to solve five challenging engineering design optimization problems and then is used to estimate the unknown parameters of three photovoltaic models. Experimental results show the solutions offered by CBSA outperform those of the compared algorithms including BSA, two recently proposed variants of BSA and other some state-of-the-art algorithms on nearly all test problems, which proves the effectiveness of the improved strategies. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： backtracking search algorithm Competitive learning Engineering optimization Photovoltaic systems Parameter identification

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backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones

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ELECTRICAL ENGINEERING 2015年第2期97卷 101-110页

作者： Kilic, Ulas Celal Bayar Univ Fac Hasan Ferdi Turgutlu Technol Dept Mechatron Engn Turgutlu Manisa Turkey

This paper presents a solution technique for optimal power flow (OPF) with valve-point effect and prohibited operating zones of power systems using backtracking search algorithm (BSA). BSA is a new population-based evolutionary algorithm. The most important property of the algorithm is not over precision to initial of value, unlike many other heuristic algorithms. The proposed algorithm having four different cases is tested on IEEE-30 bus test system. The results of BSA are compared to those reported in literature. Thus, its validity for so applications in this area is proved. In this paper, OPF problem of power systems is solved by BSA for the first time.

关键词： backtracking search algorithm Optimal power flow Valve point effect Prohibited zones

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backtracking search algorithm for color image multilevel thresholding

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第2期12卷 385-392页

作者： Pare, S. Bhandari, A. K. Kumar, A. Bajaj, V. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India GIST Sch Elect Engn & Comp Sci Gwangju South Korea Natl Inst Technol Patna 800005 Bihar India

Multilevel thresholding of the color images such as natural and satellite images becomes a challenging task due to the inherent fuzziness and ambiguity in such images. To address this issue, a modified fuzzy entropy (MFE) function is proposed in this paper. MFE function is the difference of adjacent entropies, which is optimized to provide thresholding levels such that all regions have almost equal entropies. To improve the performance of MFE, backtracking search algorithm is used. The numerical and statistical results indicate that MFE-BSA has higher peak signal-to-noise ratio, lower mean square error for all the images at different thresholding levels. Moreover, structural and feature similarity indices for MFE-BSA are closer to unity and the average fitness value obtained using MFE-BSA is minimum (lesser than 0.5). Overall, MFE-BSA shows very good segmentation results in terms of preciseness, robustness, and stability.

关键词： Multilevel thresholding Modified fuzzy entropy backtracking search algorithm

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backtracking search algorithm based on Knowledge of Different Populations for Continuous Optimization Problems 24

Backtracking Search Algorithm based on Knowledge of Differen...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Zhao, Fuqing Zhao, Jinlong Hu, Xiaotong Zhang, Yi Ma, Weimin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Xijin Univ Sch Mech Engn Xian 710123 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

ISBN: (纸本)9781728165974

backtracking search algorithm (BSA) has been applied to solve the various optimization problems in recent years. However, BSA is difficult to solve non-separable problems due to its single search mechanism. In this paper, backtracking search algorithm based on knowledge of different populations, named DKBSA, is proposed to solve continuous optimization problems. In DKBSA, sub-population partitioning method is used to enhance the local search ability and alleviate the loss rate of the diversity of population. Afterwards, a mutation strategy with knowledge guidance and rotation invariance, which is based on the current sub-population information and historical information, is designed to improve the convergence speed of the DKBSA. Furthermore, a control parameter of adaptive search factor is embedded in the mutation strategy to balance the exploitation and exploration of the proposed algorithm. Finally, a probabilistic model-based strategy is proposed to generate dominant individuals to further improve the search ability of the proposed algorithm. The experimental results of the state-of-the-art algorithms in the CEC2017 benchmark test suit reveal that the DKBSA is effective for solving non-separable problems.

关键词： backtracking search algorithm sub-population diversity rotation invariance

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Sequential quadratic programming enhanced backtracking search algorithm

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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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Multi-objective Allocation of Multi-type Distributed Generators along Distribution Networks Using backtracking search algorithm and Fuzzy Expert Rules

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第3期44卷 252-267页

作者： El-Fergany, Attia Zagazig Univ Fac Engn Dept Elect Power & Machines POB 44519 Zagazig Egypt

This article presents a methodology for a multi-objective optimal allocation of multi-type distributed generators in radial distribution networks based on a backtracking search algorithm. The study aims to prove the validity and the strength of the backtracking search algorithm multi-objective method on distributed generator allocation. The multi-objective function is expressed to minimize the network power losses, to consolidate the static voltage stability indices, and to ameliorate the bus's voltage profile. The indicators of loss sensitivity factors and bus voltage magnitudes are incorporated to establish set of fuzzy expert rules to assort the preliminary buses for distributed generator placement. The proposed methodology allows the fuzzy decision maker to decide the best compromise solution among the offered Pareto-optimal solutions. The salient features of the backtracking search algorithm are demonstrated and marked on 33- and 94-node radial distribution networks with various scenarios. The cropped results are compared with those reported by others in the literature, validating and signifying the proposed approach. The study finds that the type-3 distributed generator unit (delivers P and injects Q) is most preferred to reduce power losses along network lines and to boost both the bus voltage profile and voltage stability indices.

关键词： multi-distributed generators backtracking search algorithm multi-objective optimization fuzzy expert rules loss minimization static voltage stability indices

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