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

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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An improved backtracking search algorithm for casting heat treatment charge plan problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第3期30卷 1335-1350页

作者： Zhou, Jianxin Ye, Hu Ji, Xiaoyuan Deng, Weilin Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Hubei Peoples R China

This study investigates the optimization of the charge plan in casting heat treatment. The optimization problem is formulated as a 0-1 integer programming model aiming at maximizing the utilization of the furnaces, minimizing the holding temperature differences and the overall delivery deadline of castings in a furnace. To approach the mathematical model, a two-steps solution methodology is designed. First, the feasible casting candidate sets are generated in consideration of the holding temperature and cooling mode constraints. Then, an improved backtracking search algorithm (IBSA) is proposed to obtain optimal charge plan for each feasible candidate set. The best one among the optimal charge plans obtained by IBSA is selected as the final charge plan. In IBSA, a mapping mechanism is applied to make original backtracking search algorithm (BSA) suitable to discrete problems. Improvements that consist of the modification of historical population updating mechanism, the hybrid of mutation and crossover strategy of difference evaluation algorithm, a greedy local search algorithm and the re-initialization operator are also made to enhance the exploitation and exploration ability of IBSA. The comparisons of simulation experiments demonstrate the effectiveness of the proposed model and the performance of the proposed algorithm.

关键词： Charge plan Heat treatment backtracking search algorithm Difference evaluation Greedy local search

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A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic algorithm or modified backtracking search algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年 190卷 146-157页

作者： Vitayasak, Srisatja Pongcharoen, Pupong Hicks, Chris Naresuan Univ Fac Engn Dept Ind Engn CORIA Phitsanulok 65000 Thailand Newcastle Univ Newcastle Univ Business Sch 5 Barrack Rd Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Facility layout problems (FLP) involve determining the optimal placement of machines within a fixed space. An effective layout minimises costs. The total material travel distance is a key indicator of the efficiency of internal logistics. Changes in demand and product mix may alter the material flow. The dynamic facilities layout problem (DFLP) takes into account changes in demand and allows for the periodic redesign of facilities. Facility redesign may reduce the material flow cost, but there is a trade-off between material flow improvements and reorganisation costs. There is a limited literature on the redesign of facilities with stochastic demand, heterogeneous-sized resources and rectilinear material flow. The backtracking search algorithm (BSA) has been used to successfully solve a range of engineering problems, but it has not previously been used to solve operations management problems or the FLP. This paper outlines novel modified backtracking search algorithms (mBSAs) that solved the stochastic DFLP with heterogeneous sized resources. The combination of material flow and redesign costs were minimised. Three mBSA were benchmarked against the classical BSA and a Genetic algorithm (GA) using 11 benchmark datasets obtained from the literature. The best mBSA generated better solutions than the GA for large-size problems. The total costs for the layouts generated by the best mBSA were significantly lower than for the conventional BSA. The modifications to the BSA increased the diversity of candidate solutions, which increased the amount of exploration. The computational time required by the three mBSAs was up to 70% less than the GA. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： backtracking search algorithm Genetic algorithm Dynamic facility layout problem Stochastic demand

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Optimal power flow of two-terminal HVDC systems using backtracking search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 78卷 326-335页

作者： Ayan, Kursat Kilic, Ulas Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn Sakarya Turkey 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) of high-voltage direct current (HVDC) power systems using a backtracking search algorithm (BSA). BSA is a population-based evolutionary algorithm (EA), and it is not sensitive to initial conditions, contrary to most other meta-heuristic algorithms. The proposed algorithm is applied to three different test systems as follows: the modified 5-bus test system, the modified WSCC 9-bus test system, and the modified New England 39-bus test system. As a result of the simulations, minimum, maximum, and average production costs and CPU times are obtained for different cases of each of the three test systems. These results are also compared to those of the Artificial Bee Colony (ABC) algorithm, the Genetic algorithm (GA), and the unified method provided in literature. In regard to the comparative results, it can be said that the proposed method has a shorter CPU time and is more efficient than the others. Thus, the applicability and efficiency of the proposed method in this field are demonstrated. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： backtracking search algorithm Optimal power flow HVDC Power system

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A quantum mutation-based backtracking search algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第4期55卷 3019-3073页

作者： Nama, Sukanta Sharma, Sushmita Saha, Apu Kumar Gandomi, Amir H. Maharaja Bir Bikram Univ Dept Appl Math Agartala 799004 Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.

关键词： backtracking search algorithm Quantum Gaussian mutation Quasi-reflection Benchmark function Engineering design problem

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Multi-objective learning backtracking search algorithm for economic emission dispatch problem

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SOFT COMPUTING 2021年第3期25卷 2433-2452页

作者： Xu, Xinlin Hu, Zhongbo Su, Qinghua Xiong, Zenggang Liu, Mianfang Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Comp & Informat Sci Xiaogan Hubei Peoples R China Hunan Software Vocat Inst Sch Software & Informat Engn Xiangtan Hunan Peoples R China

The backtracking search algorithm (BSA) as a novel intelligent optimizer belongs to population-based evolutionary algorithms. In this paper, a multi-objective learning backtracking search algorithm (MOLBSA) is proposed to solve the environmental/economic dispatch (EED) problem. In this algorithm, we design two novel learning strategies: a leader-choosing strategy, which takes a sparse solution from an external archive as leader;a leader-guiding strategy, which updates individuals with the guidance of leader. These two learning strategies have outstanding performance in improving the uniformity and diversity of obtained Pareto front. The extreme solutions, compromise solution and three metrics obtained by MOLBSA are further compared with those of well-known multi-objective optimization algorithms in IEEE 30-bus 6-unit test system and 10-unit test system. Simulation results demonstrate the capability of MOLBSA in generating well-distributed and high-quality approximation of true Pareto front for the EED problem.

关键词： backtracking search algorithm Environmental economic dispatch Multi-objective optimization

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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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backtracking search algorithm with dynamic population for energy consumption problem of a UAV-assisted IoT data collection system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Zhang, Yiying Huang, Chao Huang, Hailong Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aerot & Aviat Engn Hong Kong Peoples R China

In recent years, collecting data from IoT devices by unmanned aerial vehicles (UAVs) has become a very hot research topic. This paper focuses on the energy consumption problem of a UAV-based IoT data collection system. To solve the considered energy consumption problem, this paper proposes a new population-based optimization algorithm called the backtracking search algorithm with dynamic population (BSADP), which can determine the optimal number and locations of stop points of the UAV simultaneously. In addition, BSADP has a simple framework, which consists of the proposed enhanced backtracking search algorithm (EBSA) and the designed population adjustment mechanism with opposition-based learning (PAMOBL). In the search process, the population is regarded as the entire deployment of the UAV. BSADP firstly generates the trail deployment of the UAV by EBSA and then the next generation deployment of the UAV is produced based on the trail deployment and PAMOBL. The performance of BSADP is investigated by two energy consumption formulations. Experimental results support the superiority of BSADP in optimizing the deployment of the UAV and prove the application value of BSADP in the real scenario. The source code of the proposed algorithm can be found from: https://***/jsuzyy/BSADP.

关键词： Unmanned aerial vehicles Energy consumption Internet of Things backtracking search algorithm

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backtracking search algorithm with specular reflection learning for global optimization

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106546-106546页

作者： Zhang, Yiying Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Benefiting from population, randomness and simple structures, metaheuristic methods show excellent performance for solving global optimization problems. However, in some cases, in order to get promising solutions, the existing metaheuristic methods usually need to be modified. This work reports a new technique, called specular reflection learning, for improving the optimization performance of metaheuristic methods. Specular reflection learning is motivated by specular reflection phenomenon in physics. Note that, there is a close relationship between opposition-based learning and specular reflection learning. Opposition-based learning can be seen as a special case of specular reflection learning. In order to investigate the effectiveness of specular reflection learning, specular reflection learning is employed to improve backtracking search algorithm (BSA). The performance of the proposed backtracking search algorithm with specular reflection learning is evaluated by 88 test functions extracted from the well-known CEC 2013, CEC 2014 and CEC 2017 test suites, and two constrained engineering design problems. Experimental results confirm that specular reflection learning is a more effective technique for improving BSA compared with opposition-based learning, which establishes the foundation for the applications of specular reflection learning on other metaheuristics. In addition, the source code of this work can be found from https://***/matlabcentral/fileexchange/79030-bsa_srl. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Opposition-based learning Specular reflection learning backtracking search algorithm Global optimization

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