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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Ragini Kumar, Sudhanshu Akash Nath, Apurv Singh, Shivanshu Kumar Awanish Das, Pallav Bhattacharya, Aniruddha Natl Inst Technol Dept Elect Engn Agartala 799055 India

ISBN: (纸本)9781467365031

In a large power system, the optimal control of the frequency and the active power is highly desired. For many practical purposes the required operation of the controllers in maintaining the healthy power system becomes a vital part. After performing power and frequency control within the permissible limit, a better appreciation of the operation of power systems can be obtained. It is extremely necessary that the output of the machines at any instant will be exactly equal to the load plus transmission and distribution losses on the system. If the output is higher than the demand, the machines will tend to accelerate and the frequency will rise, and vice versa. Hence the frequency is not a constant quantity, but continuously varies. These variations are normally small and have no noticeable effect on most consumers. The frequency is continuously monitored against standard time-sources and when long-term tendencies to rise or fall of frequency is noticed, the control engineers take appropriate action to regulate the generators' outputs. A continuous time transfer function model of the system for studying dynamic response for small load disturbances is presented here. A proportional-integral-derivative (PID) controller based automatic generation control scheme is applied. There are different algorithms to find out the optimum parameters of a controller like Genetic algorithm, Particle swarm optimization (PSO), backtracking search algorithm (BSA) etc. However, in this project work, BSA is employed to find optimum settings of PID controller constants for a two area power system network. Results obtained using BSA has been compared with those obtained using PSO.

关键词： Automatic Generation control backtracking search algorithm PID controller Thermal power system

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Effective LSTMs with seasonal-trend decomposition and adaptive learning and niching-based backtracking search algorithm for time series forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Wu, Yuhan Meng, Xiyu Zhang, Junru He, Yang Romo, Joseph A. Dong, Yabo Lu, Dongming Zhejiang Univ City Coll Coll Comp Sci & Technol Hangzhou Peoples R China Purdue Univ Dept Chem 560 Oval Dr West W Lafayette IN 47907 USA

Long short-term memory faces challenges in information mining and parameter selection due to inherent uncertainty and randomness. In this study, we propose a novel hybrid model called 'STL-ALN_BSA-LSTM' that aims to tackle this issue. In the proposed model, data is first prepossessed using seasonaltrend decomposition to extract informative features. Next, we propose an adaptive learning and niching-based backtracking search algorithm to enhance the robustness and learning ability of the original backtracking search algorithm, which affords a balance between exploration and exploitation. Subsequently, the weights and thresholds within each LSTM are optimized by our optimization algorithm automatically, leading to improved interpretability and accuracy of the model. Finally, the proposed optimization algorithm was evaluated using five benchmark functions, and the hybrid model was compared with sixteen baselines on two real-world datasets. For rainfall forecasting, the hybrid model achieved the root mean square error of 7.3772, mean absolute error of 2.6187, and R2 of 0.8388. For power generation forecasting, our model outperformed the second-best model by 29.6%, 34.4%, and 16 parts per thousand, respectively, as in the above metrics. These results demonstrate that the proposed hybrid model can attain higher accuracy in real-life applications than previous models, which contributes in-depth insights into parameter optimization for time series forecasting, and provides valuable suggestions in resource planning, risk reduction, and decision-making. The core code of this work can be found from https://***/zjuml/STL-AL NBSA-LSTM.

关键词： Long short-term memory backtracking search algorithm Seasonal-trend decomposition Parameter optimization Time-series forecasting

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A niching backtracking search algorithm with adaptive local search for multimodal multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 101031-101031页

作者： Hu, Zhongbo Zhou, Ting Su, Qinghua Liu, Mianfang Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hunan Univ Sci & Technol Sch Math & Comp Sci Xiangtan Hunan Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan Hunan Peoples R China

Multimodal multiobjective optimization problems,which widely exist in real-world applications, have multiple Pareto optimal sets in decision space corresponding to the same Pareto front in objective space. The key to handling such problems is locating and maintaining all Pareto optimal solutions in decision space simultaneously. The distribution of Pareto optimal solutions obtained by some existing multimodal multiobjective evolutionary algorithms is still not satisfactory. This paper proposes a niching backtracking search algorithm with adaptive local search to solve such problems. In the proposed algorithm, the affinity propagation clustering method as a parameter-free automatic niching technique is adopted to form multiple niches. A novel mutation based on affinity propagation clustering is then developed to search for more Pareto solutions within each niche. In addition, an adaptive local search strategy is designed in each niche to improve the search efficiency and the accuracy of Pareto optimal solutions. The proposed algorithm is compared with seven state-of-the-art multimodal multiobjective evolutionary algorithms on a multimodal multiobjective optimization test suite with 22 benchmark functions from CEC 2019 competition and a map-based practical application problem. The experimental results show that the proposed algorithm outperforms its competitors on 14 out of 22 benchmark functions in terms of the reciprocal of Pareto sets proximity and inverted generational distance in decision space metrics. Also, the proposed method is more effective and competitive than its competitors when solving the map-based practical application problem.

关键词： Affinity propagation clustering backtracking search algorithm Multimodal multiobjective optimization problems

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A hierarchical knowledge guided backtracking search algorithm with self-learning strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104268-104268页

作者： Zhao, Fuqing Zhao, Jinlong Wang, Ling Cao, Jie Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

To improve the performance of the backtracking search optimization algorithm (BSA), a multi-population cooperative evolution strategy guided BSA with hierarchical knowledge (HKBSA) is proposed in this paper. According to the domain knowledge of the candidates in objective space, the population is divided into the dominant population, the ordinary population and the inferior population. The information between the sub populations has interacted with the evolution processes of the three sub-populations. The individuals in the dominant population are maintained as the optimal solutions and are utilized to guide the evolution of the other two sub-populations. A multi-strategy mutation mechanism is applied to solve non-separable problems. The distribution vector of inferior individuals is constructed by sampling, and a mechanism of the individual generation with feedback is proposed by combining self-learning strategy and elite learning strategy. The convergence of HKBSA is analyzed with the Markov model. Compared with the state-of-the-art BSA variants, HKBSA outperforms other algorithms in terms of the speed of convergence, solution accuracy and stability.

关键词： backtracking search algorithm Hierarchical knowledge Multi-strategy mutation Probability vector Self-learning strategy

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Boosting particle swarm optimization by backtracking search algorithm for optimization problems

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Nama, Sukanta Saha, Apu Kumar Chakraborty, Sanjoy Gandomi, Amir H. Abualigah, Laith Gomati District Polytech Dept Sci & Humanities Udaipur 799013 Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala 799046 Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia 799155 Tripura India Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh City Vietnam Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Adjusting the search behaviors of swarm-based algorithms during their execution is a fundamental errand for addressing real-world global optimizing challenges. Along this line, scholars are actively investigating the un-visited areas of a problem domain rationally. Particle Swarm Optimization (PSO), a popular swarm-based optimization algorithm, is broadly applied to resolve different real-world problems because of its more robust searching capacity. However, in some situations, due to an unbalanced trade-off between exploitation and exploration, PSO gets stuck in a suboptimal solution. To overcome this problem, this study proposes a new ensemble algorithm called e-mPSOBSA with the aid of the reformed backtracking search algorithm (BSA) and PSO. The proposed technique first integrates PSO's operational potential and then introduces BSA's exploration capability to help boost global exploration, local exploitation, and an acceptable balance during the quest process. The IEEE CEC 2014 and CEC 2017 test function suite was considered for evaluation. The outcomes were contrasted with 26 state-of-the-art algorithms, including popular PSO and BSA variants. The convergence analysis, diversity analysis, and statistical test were also executed. In addition, the projected e-mPSOBSA was employed to evaluate four unconstrained and seven constrained engineering design problems, and performances were equated with various algorithms. All these analyses endorse the better performance of the suggested e-mPSOBSA for global optimization tasks, search performance, solution accuracy, and convergence rate.

关键词： Particle swarm optimization backtracking search algorithm Global optimization IEEE CEC2014 Engineering design problem

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An optimal block knowledge driven backtracking search algorithm for distributed assembly No-wait flow shop scheduling problem

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APPLIED SOFT COMPUTING 2021年 112卷 107750-107750页

作者： Zhao, Fuqing Zhao, Jinlong Wang, Ling Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

The distributed assembly flow shop scheduling problem (DAFSP) is an important scenario in manufacturing system. In this paper, an optimal block knowledge driven backtracking search algorithm (BKBSA) is proposed to solve the distributed assembly No-wait flow shop scheduling problem (DANWFSP) with the objective of minimizing the completion time of assembly process. In BKBSA, three constructive heuristics are proposed to generate a competitive initial solution. Block-shifting based on block knowledge is embedded in the mutation strategy of BKBSA. The proposed block-shifting ensures that the optimal subsequence of a candidate solution is not destroyed in the mutation operation. The similarity between candidate solutions is utilized as feedback indicator to control the utilization of block-shifting. In addition, the VND algorithm based on factory-to-factory is proposed to further improve the optimal solution. Finally, the BKBSA and the other three state-of-the-art algorithms for DANWFSP are tested on 810 large-scale instances and 900 small-scale instances. The statistical analysis results show that BKBSA is an effective algorithm to solve DANWFSP. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Distributed assembly No-wait flow shop scheduling Job block knowledge Constructive heuristics backtracking search algorithm

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Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and backtracking search algorithm

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ENERGIES 2019年第19期12卷 3651页

作者： Kaboli, S. Hr. Aghay Al Hinai, Amer Al-Badi, A. H. Charabi, Yassine Al Saifi, Abdulrahim Sultan Qaboos Univ Sustainable Energy Res Ctr Muscat 123 Oman Sultan Qaboos Univ Dept Elect & Comp Engn Coll Engn Muscat 123 Oman Sultan Qaboos Univ Ctr Environm Studies & Res Muscat 123 Oman Petr Dev Oman PDO Muscat 100 Oman

The electromagnetic interference (EMI) generated by high voltage power systems can cause a serious problem for nearby electrically conductive structures, such as railroads, communication lines, or pipelines, that would place a system's integrity and the operational safety of the structure at high level of risk. According to the IEEE standard-80, by implementing a well-designed mitigation system, the induced voltage on neighboring electrically conductive structure can reach a harmless level. The mitigation system can enhance the overall integrity of pipelines and provide higher operation safety for personal during working on the exposed parts of metallic pipelines or conductive appurtenances. An accurate prediction about the level of induced voltage is absolutely necessary to design a suitable mitigation system for metallic pipelines. Thus, in this work a hybrid prediction methodology composed of an adaptive neuro-fuzzy inference system (ANFIS) and a backtracking search algorithm (BSA) is developed to accurately predict the electromagnetic inference's effects on metallic pipelines with shared right-of-way (RoW) and high voltage overhead lines (OHLs). Through the combination of BSA as a robust and efficient optimization algorithm in the learning process of an ANFIS approach, a hybrid data mining algorithm has been developed to predict the induced voltage on mitigated and unmitigated pipelines more accurately and reliably. The simulation results are validated by data sets observed from the Current Distribution, Electromagnetic Interference, Grounding and Soil Structure Analysis (CDEGS) software. From the simulation results it was confirmed that the proposed hybrid method is effective in accurately predicting the induced voltage on pipelines with changing system parameters. Furthermore, to evaluate the precision and applicability of the developed approach in this paper, its estimates are compared with the results obtained from an artificial neural network (ANN), a suppo

关键词： adaptive neuro-fuzzy inference system backtracking search algorithm electromagnetic interference overhead line pipeline

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A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107960-107960页

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

The flexible shop scheduling problem (FJSSP) has been an extensively studied and applied manufacturing systems in recent years. However, research has mostly focused on addressing the FJSSP in its basic form by relaxing several practical constraints. In this work, we consider FJSSP with worker flexibility and uncertain processing times. A simheuristic approach addresses the multi-objective stochastic FJSSP with worker flexibility, minimizing the expected makespan and standard deviation of makespan. This approach integrates the Monte Carlo simulation (MCS) into the proposed multi-objective modified backtracking search algorithm (MBSA) framework. MBSA employs an effective population initialization strategy, dynamic mutation and crossover operators, and a transfer criterion. MCS evaluates' promising' solutions generated by MBSA to guide the search process. Extensive experiments are performed considering three FJSSP benchmark data sets. At first, the best parameters for the MBSA are identified using the Taguchi method. Then, for the deterministic variant of the problem, MBSA is compared with two other metaheuristics to demonstrate its effectiveness. Finally, the stochastic variant is investigated considering two factors: variation level and the probability distribution of the processing times. Computational results statistically show a significant effect of these factors on the objectives, demonstrating the importance of selecting an appropriate probability distribution in an uncertain environment. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Flexible job shop scheduling problem Uncertain processing times Simheuristic approach Worker flexibility backtracking search algorithm

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Enhancing backtracking search algorithm using Reflection Mutation Strategy Based on Sine Cosine

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algorithmS 2019年第11期12卷 225-225页

作者： Zhou, Chong Li, Shengjie Zhang, Yuhe Chen, Zhikun Zhang, Cuijun Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Lab Artificial Intelligence & Machine Learning Shijiazhuang 050031 Hebei Peoples R China Beibu Gulf Univ Coll Resources & Environm Qinzhou 535011 Peoples R China

backtracking search algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.

关键词： evolutionary algorithm backtracking search algorithm reflection mutation sine cosine unit simplex

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A New Hybridized backtracking search Optimization algorithm with Differential Evolution for Sidelobe Suppression of Uniformly Excited Concentric Circular Antenna Arrays

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2015年第3期25卷 262-268页

作者： Das, Sudipta Mandal, Durbadal Kar, Rajib Ghoshal, Sakti Prasad Natl Inst Technol Dept Elect & Commun Engn Durgapur India Natl Inst Technol Dept Elect Engn Durgapur India

In this work, a hybridized combination of backtracking search algorithm (BSA) with differential evolution (DE) is proposed and applied on sidelobe suppression problem of uniformly excited concentric circular antenna arrays (CCAA). Each array is assumed to have isotropic elements and be placed on x-y plane and has one center element. A search for optimal setting of ring radius and number of elements in each ring is carried out so as to achieve low sidelobe performance on overall azimuth plane. Care has been taken so that directivity does not get degraded as far as possible. Before applying this algorithm to CCAA design problem, BSA and the hybridized algorithm BSA-DE are tested on five complex benchmark functions. Based on 30-independent runs for each benchmark function, Wilcoxon's pairwise signed ranks test is utilized to judge the relative search performance of these two algorithms. The hybridized algorithm proves its superiority on almost all the considered benchmark functions. For the CCAA design problem dealt with in this work, BSA-DE shows its superiority on one or both pattern parameters as well as structural parameters. (c) 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:262-268, 2015.

关键词： backtracking search algorithm differential evolution mutation operator concentric circular antenna arrays sidelobe suppression

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