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Enhanced backtracking search algorithm for Optimal Coordination of Directional Over-current Relays Including Distributed Generation

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第3期44卷 278-290页

作者： Othman, Ahmed M. Abdelaziz, Almoataz Y. Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt Ain Shams Univ Dept Elect Power & Machines Fac Engn Abdo Basha Sq Cairo 11517 Egypt

In this article, the enhanced backtracking search algorithm is employed to achieve optimal coordination of directional over-current relays. A novel objective function is formulated to minimize the total operating times while maintaining the validity of the coordination time interval. The proposed technique is applied to optimize the influential variables of the coordination problem, which are plug tap setting, time dial setting, and type of inverse relay characteristics. Both old electromechanical and digital relays are considered in the study. The enhanced backtracking search algorithm is a recent heuristic-based optimization, and its performance in solving relay coordination problem is compared with other well-established algorithms to demonstrate its viability and effectiveness.

关键词： directional over-current relays optimal coordination plug tap setting time dial setting backtracking search algorithm

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Improving backtracking search algorithm with variable search strategies for continuous optimization

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APPLIED SOFT COMPUTING 2019年 80卷 567-578页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

The backtracking search algorithm (BSA), a relatively new evolutionary algorithm (EA), has been shown to be a competitive alternative to other population-based algorithms. To effectively solve a variety of optimization problems, this paper suggests ten mutation strategies and compares the performance of selection mechanisms in employing these strategies. Moreover, following the original BSA design, new parameters of historical mean and best positions are proposed in order to implement several additional mutation strategies. In addition, as recommended in the literature, a one-dimensional crossover scheme is enacted for greedy strategies in order to prevent premature convergence. Furthermore, three settings for search factors of mutation strategies are proposed. As a result, improved BSA versions that employed, respectively, ten and four mutation strategies were found to significantly facilitate the ability of BSA to handle optimization tasks of different characteristics. The experimental results show that the proposed versions outperformed the basic BSA in terms of achieving high convergence speed in the early stage, reaching the convergence precision and plateau with better scores, and performing perfectly on tests of composition functions. In addition, the improved BSA versions outperformed five popular, nature-inspired algorithms in terms of achieving the best convergence precision and performing perfectly on six composition functions. (C) 2019 Elsevier B.V. All rights reserved.

关键词： backtracking search algorithm Continuous optimization Mutation strategies search factors One-dimensional crossover

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An improved backtracking search algorithm for the flexible job shop rescheduling problem with new job insertions

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2022年第1期16卷 41-70页

作者： Caldeira, Rylan H. Gnanavelbabu, A. Anna Univ Dept Ind Engn CEG Campus Chennai 600025 Tamil Nadu India

In real-world environments, production schedules are subject to several disruptions. Hence it is essential to account for these disruptions while constructing the production schedules. This work considers the flexible job-shop rescheduling problem (FJSRP) considering new job insertions. An improved discrete backtracking search algorithm and a slack-based inserting rescheduling strategy are proposed to address this problem considering makespan as objective. A set of heuristics is used to generate a diverse initial population. An order-preserving crossover and a mutation operator is developed to balance the exploitation and exploration. A transfer criterion is utilised to employ the information of the past population. The algorithm's exploitation capability is enhanced by employing a local search technique. Extensive computational work is performed on well-known benchmark instances. Computational results demonstrate the superiority of the proposed approach as well as the rescheduling strategy. [Received: 7 July 2020;Accepted: 27 January 2021]

关键词： backtracking search algorithm flexible job shop rescheduling problem job insertion local search technique rescheduling strategy

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Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

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MEASUREMENT 2016年 78卷 49-62页

作者： Abd Ali, Jamal Hannan, M. A. Mohamed, Azah Abdolrasol, Maher G. M. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Minist Elect Gen Co Elect Prod Middle Reg Baghdad Iraq

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic control backtracking search algorithm Gravitational search algorithm Particle swarm optimization Induction motor Scalar controller

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An Enhanced backtracking search algorithm for the Flight Planning of a Multi-Drones-Assisted Commercial Parcel Delivery System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 11396-11409页

作者： Zhang, Yiying Zhou, Guanzhong Hang, Peng Huang, Chao Huang, Hailong Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Sports Sci & Technol RISports Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Tongji Univ Dept Traff Engn Shanghai 200092 Peoples R China

Using drones to carry out commercial parcel delivery can significantly promote the transformation and upgrading of the logistics industry thanks to the saving of human labor source, which is becoming a new component of intelligent transportation systems. However, the flight distance of drones is often constrained due to the limited battery capacity. To address this challenge, this paper designs a multi-drones-assisted commercial parcel delivery system, which supports long-distance delivery by a generalized service network (GSN). Each node of the GSN is equipped with charging piles to provide a charging service for drones. Given the limited number of charging piles at each node and the limited battery capacity of a drone, to ensure the efficient operation of the system, the flight planning problem of drones is converted into a large-scale optimization problem by a priority-based encoding mechanism. To solve this problem, an enhanced backtracking search algorithm (EBSA) is reported, which is inspired by the characteristics of the considered flight planning problem and the weak ability of the backtracking search algorithm to escape from a local optimum. The core components of EBSA are the designed comprehensive learning mechanism and local escape operator. Experimental results prove the validity of the improved strategies and the excellent performance of EBSA on the considered flight planning problem.

关键词： Drones Batteries Planning search problems backtracking Task analysis Metaheuristics Parcel delivery flight planning multi-drones generalized service network backtracking search algorithm

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An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106863-106863页

作者： Caldeira, Rylan H. Gnanavelbabu, A. Vaidyanathan, T. Anna Univ Dept Ind Engn CEG Campus Chennai 600025 Tamil Nadu India

This work addresses the flexible job shop scheduling problem considering new job arrivals which is a common occurrence in real-world manufacturing enterprises. With growing concerns regarding the increasing energy consumption and the advent of green manufacturing, it is essential to consider energy-related objectives in scheduling. Hence, we formulate a mathematical model for the flexible job shop scheduling problem considering new job arrivals and turn on/off strategy with an objective to minimize the makespan, energy consumption, and instability. We address this problem by using an improved backtracking search algorithm. We propose an effective crossover operator, to improve the algorithm's searchability and prevent premature convergence. A slack-based insertion rescheduling strategy is developed to handle new job insertions in the schedule. Taguchi analysis is employed to identify the best combination of algorithm parameters. Finally, we generate new benchmark instances for the flexible job shop scheduling problem with new job arrivals. We validate the superior performance of the proposed insertion strategy and the algorithm in terms of solution quality through comprehensive experiments.

关键词： Flexible job shop scheduling backtracking search algorithm New job arrivals Energy consumptions

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Centroid opposition-based backtracking search algorithm for global optimization and engineering problems

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ADVANCES IN ENGINEERING SOFTWARE 2024年 198卷

作者： Debnath, Sanjib Debbarma, Swapan Nama, Sukanta Saha, Apu Kumar Dhar, Runu Yildiz, Ali Riza Gandomi, Amir H. Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala 799046 Tripura India ICFAI Univ Tripura Dept Comp Sci & Engn Kamalghat 799210 Tripura India Gomati Dist Polytech Dept Sci & Humanities Udaipur 799013 Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Maharaja Bir Bikram Univ Dept Math Agartala 799004 Tripura India Bursa Uludag Univ Dept Mech Engn Bursa Turkiye Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Evolutionary algorithms (EAs) have a lot of potential to handle nonlinear and non-convex objective functions. Particularly, the backtracking search algorithm (BSA) is a popular nature-based evolutionary optimization method that has attracted many researchers due to its simple structure and efficiency in problem-solving across diverse fields. However, like other optimization algorithms, BSA is also prone to reduced diversity, local optima, and inadequate intensification capabilities. To overcome the flaws and increase the performance of BSA, this research proposes a centroid opposition-based backtracking search algorithm (CoBSA) for global optimization and engineering design problems. In CoBSA, specific individuals simultaneously acquire current and historical population knowledge to preserve population variety and improve exploration capability. On the other hand, other individuals execute the position from the current population's centroid opposition to progress convergence speed and exploitation potential. In addition, an elite process based on logistic chaotic local search was developed to improve the superiority of the current individuals. The suggested CoBSA was validated on a set of benchmark functions and then employed in a set of application examples. According to extensive numerical results and assessments, CoBSA outperformed the other state-of-the-art methods in terms of accurateness, reliability, and execution capability.

关键词： Centroid opposition-based learning (CODL) backtracking search algorithm Multiple learning Chaos elite strategy Engineering design problem

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Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models

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APPLIED ENERGY 2018年 226卷 408-422页

作者： Yu, Kunjie Liang, J. J. Qu, B. Y. Cheng, Zhiping Wang, Heshan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Henan Peoples R China

Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.

关键词： Parameter identification Photovoltaic model backtracking search algorithm Multiple learning

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Study impact of various load models on DG placement and sizing using backtracking search algorithm

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APPLIED SOFT COMPUTING 2015年 30卷 803-811页

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

In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation. (C) 2015 Elsevier By. All rights reserved.

关键词： Distributed generation Network losses Load models backtracking search algorithm

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Parameter Identification of Nonlinear Muskingum Model with backtracking search algorithm

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WATER RESOURCES MANAGEMENT 2016年第8期30卷 2767-2783页

作者： Yuan, Xiaohui Wu, Xiaotao Tian, Hao Yuan, Yanbin Adnan, Rana Muhammad 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

Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.

关键词： Nonlinear Muskingum model Parameter identification backtracking search algorithm Flood routing Wilcoxon signed ranks test

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