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

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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Dynamic reconfiguration of active distribution network based on improved backtracking search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1469-1480页

作者： Li, Wenying Guo, Qinghong Wen, Ming Zhang, Yun Pan, Xin Xiao, Zhenfeng Yang, Shuzhi State Grid Hunan Prov Elect Power Co Econ & Technol Res Inst Changsha Peoples R China Hunan Prov Key Lab Energy Internet Supply & Dema Changsha Peoples R China State Grid Hunan Prov Elect Power Co Changsha Peoples R China

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system.

关键词： Active distribution network distributed power sources demand-side response dynamic reconfiguration backtracking search algorithm

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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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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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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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A Bio-Inspired Multi-Population-Based Adaptive backtracking search algorithm

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COGNITIVE COMPUTATION 2022年第2期14卷 900-925页

作者： Nama, Sukanta Saha, Apu Kumar MBB Univ Dept Math Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala Tripura India

backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm's performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm's execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.

关键词： backtracking search algorithm Mutation strategies Multi-population strategy CEC2014 CEC2017

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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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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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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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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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