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Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm

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JOURNAL OF CLEANER PRODUCTION 2017年 144卷 228-238页

作者： Lu, Chao Gao, Liang Li, Xinyu Pan, Quanke Wang, Qi Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Permutation flow shop scheduling problems (PFSPs) have been extensively studied because of its broad industrial applications. However, setup and transportation time are usually ignored in most research, which causes a huge gap between the theoretical research and practical application. Meanwhile, energy saving has attracted growing attention due to the advent of sustainable manufacturing. Thus, we investigate an energy-efficient PFSP with sequence-dependent setup and controllable transportation time from a real-world manufacturing enterprise. First of all, a novel multi-objective mathematical model considering both makespan and energy consumption is formulated based on a comprehensive investigation. Then, a hybrid multi-objective backtracking search algorithm (HMOBSA) is proposed to solve this problem. Furthermore, a new energy saving scenario is developed to simultaneously ensure the service span of machines and energy saving. Finally, to evaluate the effectiveness of the proposed HMOBSA and energy saving scenario, we compare our proposal with other two famous multi-objective algorithms including NSGA-II and MOEA/D by conducting a real-world case study. The experimental results indicate that the proposed HMOBSA is superior to NSGA-II and MOEA/D for this case. Additionally, the proposed energy saving scenario also outperforms its competitors. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy efficiency Permutation flow shop scheduling Controllable transportation time backtracking search algorithm Multi-objective optimization

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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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Solving non-convex economic dispatch problem via backtracking search algorithm

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ENERGY 2014年 77卷 372-381页

作者： Modiri-Delshad, Mostafa Abd Rahim, Nasrudin Wisma R&D Univ Malaya UM Power Energy Dedicated Adv Ctr UMPE DAC Kuala Lumpur 59990 Malaysia King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This paper presents BSA (backtracking search algorithm) for solving of ED (economic dispatch) problems (both convex and non-convex) with both the valve-point effects in the generator cost function and the transmission network loss considered. BSA is a new evolutionary algorithm for solving of numerical optimization problems;it uses a single control parameter and two crossover and mutation strategies for powerful exploration of the problem's search space. Four test systems (with 3, 6, 20, and 40 generators) are the case studies verifying the method's robustness and effectiveness. The results confirm that compared with existing well-known methods and especially in large-scale test systems, the proposed algorithm is the better approach to solving ED problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch backtracking search algorithm Valve point effect Transmission loss Non-convex Evolutionary

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A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第21期39卷 11165-11174页

作者： Askarzadeh, Alireza Coelho, Leandro dos Santos Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Parana Brazil

Polarization curves are vital tools for simulation, evaluation, management, and optimization of proton exchange membrane fuel cells (PEMFCs). They are affected by various parameters related to the nonlinearities associated with the electrochemical processes governing fuel cells (FCs). However, the relative importance and effect of each parameter on the polarization curve is different. In order to efficiently estimate the unknown parameters of the PEMFC electrochemical-based model and obtain accurate solutions, in this paper, a backtracking search algorithm combined with Burger's chaotic map (BSABCM) is proposed and investigated. The results related to two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, illustrate the effectiveness of the proposed methodology. Furthermore, the BSABCM presented competitive results when compared with the results of the other metaheuristics. Copyright (C) 2014, Hydrogen Energy Publications, LLC: Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cells backtracking search algorithm Parameter estimation

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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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An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system

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NEUROCOMPUTING 2017年 237卷 175-184页

作者： Islam, Naz Niamul Hannan, M. A. Shareef, Hussain Mohamed, Azah Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Kajang 43000 Selangor Malaysia United Arab Emirates Univ Dept Elect Engn F-1 BldgLevel 1 Coll EngnPOB 15551 Al Ain U Arab Emirates

This paper deals with the backtracking search algorithm (BSA) optimization technique to solve the design problems of multi-machine power system stabilizers (PSSs) in large power system. Power system stability problem is formulated by an optimization problem using the LTI state space model of the power system. To conduct a comprehensive analysis, two test systems (2-AREA and 5-AREA) are considered to explain the variation of design performance with increase in system size. Additionally, two metaheuristic algorithms, namely bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) are accounted to evaluate the overall design assessment. The obtained results show that BSA is superior to find consistent solution than BFOA and PSO regardless of system size. The damping performance that achieved from both test systems are sufficient to achieve fast system stability. System stability in linearized model is ensured in terms of eigenvalue shifting towards stability regions. On the other hand, damping performance in the non-linear model is evaluated in terms of overshoot and setting times. The obtained damping in both test systems ate stable for BSA based design. However, BFOA and PSO based design perform worst in case of large power system. It is also found that the performance of BSA is not affected for large numbers of parameter optimization compared to PSO, and BFOA optimization techniques. This unique feature encourages recommending the developed backtracking search algorithm for PSS design of large multi-machine power system.

关键词： Power system stability Power system oscillations Power system damping Power system stabilizer backtracking search algorithm Multi-machine power system

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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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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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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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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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