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9th Annual IEEE International Systems Conference (SysCon)

作者： Precup, Radu-Emil Balint, Alexandra-Dina Radac, Mircea-Bogdan Petriu, Emil M. Politehn Univ Timisoara Dept Automat & Appl Informat Timisoara Romania Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781479959273

This paper suggests the backtracking search optimization algorithm ( BSOA)-based tuning of the parameters of proportional-integral-derivative ( PID) controllers for a pancake direct current ( DC) torque motor systems. These motors are important in the framework of Diesel engine exhaust gas recirculation valves in automotive applications. The BSOAs solve optimization problems that minimize objective functions expressed as the weighted sum of overshoot plus the integral of squared control error. The parameters of the PID controllers are the elements of the vector variables of the objective functions. The impact of the parameter that controls the amplitude of the search direction matrix is analyzed in terms of a digitally simulated case study that performs the shaft angle control.

关键词： backtracking search optimization algorithm optimization problems PID controllers search direction matrix torque motor systems

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backtracking search optimization algorithm-based least square support vector machine and its applications

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 94卷 103801-103801页

作者： Tian, Zhongda Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

Based on statistical learning theory, least square support vector machine can effectively solve the learning problem of small samples. However, the parameters of the least square support vector machine model have a great influence on its performance. At the same time, there is no clear theoretical basis for how to choose these parameters. In order to cope with the parameters optimization of the least square support vector machine, a backtracking search optimization algorithm-based least square support vector machine model is proposed. In this model, backtracking search optimization algorithm is introduced to optimize the parameters of the least square support vector machine. Meanwhile, the least square support vector machine model is updated by the prediction error combined with the sliding window strategy to solve the problem of mis-match between the prediction model and the actual sample data in the time-varying system. The performance of the proposed model is verified by classification and regression problems. The classification performance of the model is verified by five Benchmark datasets, and the regression prediction performance is verified by the dynamic liquid level of the oil production process. Compared with genetic algorithm, particle swarm optimization algorithm, and improved free search algorithm optimized least square support vector machine, the simulation results show that the proposed model has higher classification accuracy with less computation time, and higher prediction accuracy and reliability for the dynamic liquid level. The proposed model is effective.

关键词： backtracking search optimization algorithm Least square support vector machine Dynamic fluid level Benchmark dataset Classification Prediction

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Peak-to-average power ratio reduction using backtracking search optimization algorithm in OFDM systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第4期24卷 2307-2316页

作者： Taspinar, Necmi Tokur Bozkurt, Yuksel Erciyes Univ Dept Elect & Elect Engn Fac Engn Kayseri Turkey Gaziantep Univ Vocat Sch Higher Educ Gaziantep Dept Elect & Automat Gaziantep Turkey

A peak-to-average power ratio (PAPR) reduction method for an orthogonal frequency division multiplexing (OFDM) system based on a combination of a partial transmit sequence (PTS) scheme with the backtracking search optimization algorithm (BSA) is proposed. The PTS scheme shows good PAPR reduction performance;however, it requires a complex computation to search the set of optimum phase factors, especially for high-speed data transmissions. To minimize the complexity of the search for optimum phase factors, the PTS scheme is combined with the BSA. The BSA is a new and efficient population-based evolutionary search algorithm for real-valued optimization problems. A set of simulations is conducted for the PAPR reduction performance and computational complexity comparisons of the BSA-PTS scheme with different PTS schemes. Simulation results indicate that the BSA-PTS scheme yields an effective PAPR reduction performance with low computational complexity.

关键词： Orthogonal frequency division multiplexing backtracking search optimization algorithm partial transmit sequence peak-to-average power ratio

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Wire Fault Diagnosis Based on Time-Domain Reflectometry and backtracking search optimization algorithm

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2016年第4期31卷 340-347页

作者： Boudjefdjouf, Hamza Bouchekara, Houssem R. E. H. de Paulis, Francesco Smail, Mostafa K. Orlandi, Antonio Mehasni, Rabia Univ Freres Mentouri Constantine Dept Elect Engn Constantine Elect Engn Lab LEC Constantine 25000 Algeria Univ Freres Mentouri Constantine Dept Elect Engn LGEC Lab Elect Engn Constantine Constantine 25000 Algeria Dept Ind & Informat Engn & Econ UAq EMC Lab Via G Gronchi 18 I-67100 Laquila Italy IPSA 15-21 Rue Maurice Grandcoing F-94200 Ivry France

The development of a robust and accurate fault diagnosis approach under various system and fault conditions is a research area of great interest. The objective of this paper is to develop a new non-destructive approach for wiring diagnosis based on Time Domain Reflectometry (TDR) in one hand and on backtracking search optimization algorithm (BSA) in the other hand. Real-world case studies are investigated to demonstrate the effectiveness and robustness of the proposed approach. Simulation results evaluated from experimental data demonstrate that the proposed approach can be used for effective diagnosis of complex wiring networks.

关键词： backtracking search optimization algorithm time domain reflectometry wiring diagnosis

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An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 2797-2831页

作者： Zaman, Hamid Reza Rafat Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm's problems that BSA's mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.

关键词： Particle swarm optimization backtracking search optimization algorithm Solving continuous optimization problems Hybridization

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Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年第Jan.期64卷 1197-1205页

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

In this article, a very recently swarm optimization technique namely a backtracking search optimization algorithm (BSOA) is addressed to assign the distributed generators (DGs) along radial distribution networks. One of the main features of the BSOA is a single control parameter and not over sensitive to the initial value of this factor. The objective function is adapted with weighting factor to reduce the network real loss and enhance the voltage profile with the purpose of improving the operating performance. In addition, the combined power factor and reduction in network reactive power loss are spotted. Set of fuzzy expert rules using loss sensitivity factors and bus voltages are employed to identify the initial DG's locations. The proposed approach is attuned to tackle the shortfall of loss sensitivity factors and to decide the final placement of the DGs. Two types of the DGs are studied and investigated. The proposed method is demonstrated and validated thru many radial distribution networks with different sizes and complexities. The BSOA-based methodology can efficiently generate high-quality solutions compared to other competitive techniques in the literature. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Distributed generation Loss minimization Cumulative voltage deviations Loss sensitivity factors backtracking search optimization algorithm

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Advanced backtracking search optimization algorithm for a new joint replenishment problem under trade credit with grouping constraint

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Wang, Lin Peng, Lu Wang, Sirui Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China

In the real business situation, suppliers usually provide retailers with forward financing to decrease inventory or increase demand. Moreover, some heterogeneous goods are not allowed to transport together, or a penalty cost is incurred when heterogeneous goods are transported at the same time. This research proposes a practical multi-item joint replenishment problem (JRP) by considering trade credit and grouping constraint in accordance with the practical situation. The JRP aims to find reasonable item replenishment frequencies and each group's basic replenishment cycle time so that the overall cost can be minimized. Four intelligent algorithms, which include an advanced backtracking search optimization algorithm (ABSA), genetic algorithm (GA), differential evolution (DE) and backtracking search optimization algorithm (BSA), are provided to solve this problem. Findings of contrastive example verify that ABSA is superior to GA, DE, and BSA, which have been validated to be effective algorithms. Randomly generated problems are used to test the performance of ABSA. Results indicate ABSA is more effective and stable to resolve the proposed JRP than the other algorithms. ABSA is a good solution for the proposed JRP with heterogeneous items under trade credits. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Joint replenishment problem Trade credit Grouping constraint backtracking search optimization algorithm

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Parameter identification of chaotic systems using a shuffled backtracking search optimization algorithm

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SOFT COMPUTING 2018年第24期22卷 8317-8339页

作者： Ahandani, Morteza Alinia Ghiasi, Amir Rikhtehgar Kharrati, Hamed Univ Tabriz Dept Control Engn Fac Elect & Comp Engn Tabriz Iran

An accurate mathematical model has a vital role in controlling and synchronization of chaotic systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. This paper proposes two simple but effective estimation methods to detect the unknown parameters of chaotic models. These methods focus on improving the performance of a recently proposed evolutionary algorithm called backtracking search optimization algorithm (BSA). In this research firstly, a new operator to generate initial trial population is proposed. Then a group search ability is provided for the BSA by proposing a shuffled BSA (SBSA). Grouping population into several sets can provide a better exploration of search space, and an independent local search of each group increases exploitation ability of the BSA. Also new proposed operator to generate initial trial population, by providing a deep search, increases considerably the quality of solutions. The superiority of the proposed algorithms is investigated on parameter identification of 10 typical chaotic systems. Practical experiences and nonparametric analysis of obtained results show that both of the proposed ideas to improve performance of original BSA are very effective and robust so that the BSA by aforementioned ideas produces similar and promising results over repeated runs. A considerably better performance of proposed algorithms based on average of objective functions demonstrates that the proposed ideas can evolve robustness and consistence of BSA. A comparison of the proposed algorithms in this study with respect to other algorithms reported in the literature confirms a considerably better performance of proposed algorithms.

关键词： Chaotic models backtracking search optimization algorithm Group search Shuffled BSA

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backtracking search optimization algorithm with dual scatter search strategy for automated test case generation

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第7期35卷

作者： Ren, Jingru Zhu, Wenming Shenzhen Inst Informat Technol Dept Interdisciplinary Shenzhen Guangdong Peoples R China

It is a challenge to design an effective algorithm utilizing problem features in automated test case generation for path coverage (ATCG-PC). A feature of ATCG-PC "similar paths are usually executed by similar test cases" was touched by a few scholars and can be further exploited to design more effective algorithms. Inspired by this feature, this paper proposes a two-stage local search strategy, denoted dual scatter search (DS) strategy, which concatenates two improved scatter search strategies with different search behaviors. The first stage aims to fully exploit the discovered test cases to search for desired test cases, and the latter stage aims to mine the unexploited areas of the first stage via using less computational overhead. Then, a backtracking search optimization algorithm with dual scatter search strategy (BSADS) is proposed, which incorporates DS strategy into the backtracking search optimization algorithm (BSA) with strong exploration capability. BSA is first introduced into the field of ATCG-PC. The performance of BSA-DS and some state-of-the-art algorithms is tested on twelve popular benchmark programs. Experimental studies demonstrate that BSA-DS achieves the highest path coverage with the fewest test cases and running time on at least eight out of the twelve programs.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Automated test case generation backtracking search optimization algorithm Dual scatter search strategy Path coverage

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Optimizing echo state network with backtracking search optimization algorithm for time series forecasting

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 81卷 117-132页

作者： Wang, Zhigang Zeng, Yu-Rong Wang, Sirui Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China YGsoft Inc Yuanguang Software Pk Technol & Innovat Coast Zhuhai 519085 Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

The echo state network (ESN) is a state-of-the art reservoir computing approach, which is particularly effective for time series forecasting problems because it is coupled with a time parameter. However, the linear regression algorithm commonly used to compute the output weights of ESN could usually cause the trained network over-fitted and thus obtain unsatisfactory results. To overcome the problem, we present four optimized ESNs that are based on the backtracking search optimization algorithm (BSA) or its variants to improve generalizability. Concretely, we utilize BSA and its variants to determine the most appropriate output weights of ESN given that the optimization problem is complex while BSA is a novel evolutionary algorithm that effectively unscrambles optimal solutions in complex spaces. The three BSA variants, namely, adaptive population selection scheme (APSS)-BSA, adaptive mutation factor strategy (AMFS)-BSA, and APSS&AMFS-BSA, were designed to further improve the performance of BSA. Time series forecasting experiments were performed using two real-life time series. The experimental results of the optimized ESNs were compared with those of the basic ESN without optimization, and the two other comparison approaches, as well as the other existing approaches. Experimental results showed that (a) the results of the optimized ESNs are more accurate than that of basic ESN and (b) APSS&AMFS-BSA-ESN nearly outperforms basic ESN, the three other optimized ESNs, the two comparison approaches, and other existing optimization approaches.

关键词： Echo state network Evolutionary algorithm backtracking search optimization algorithm Time series forecasting

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