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Single Solution optimization Mechanism of teaching-learning-based optimization with Weighted Probability Exploration for Parameter Estimation of Photovoltaic Models

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Journal of Bionic Engineering 2024年第5期21卷 2619-2645页

作者： Jinge Shi Yi Chen Zhennao Cai Ali Asghar Heidari Huiling Chen Institute of Big Data and Information Technology Wenzhou UniversityWenzhou325000China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)*** objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion *** combines adaptive parameter w,Single Solution optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of *** algorithm achieves a balance between exploitation and exploration throughout the iteration *** SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior *** WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local *** algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV *** results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module *** also exhibits robust performance under varying irradiation and temperature *** study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.

关键词： teaching-learning-based optimization Single solution optimization Solar energy Photovoltaic models Weighted probability exploration

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A reinforcement learning-based ranking teaching-learning-based optimization algorithm for parameters estimation of photovoltaic models

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 93卷

作者： Wang, Haoyu Yu, Xiaobing Lu, Yangchen Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteorol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing Peoples R China

Accurate parameter control and optimization are vital issues in the process of utilizing solar energy through photovoltaic systems, which poses significant challenges due to the inherent complexity of photovoltaic systems. The paper proposes a new algorithm, reinforcement learning-based ranking teaching-learning-based optimization, for identifying photovoltaic model parameters. Parameters play a major role in the performance of many optimization algorithms, and the same parameter is not appropriate for all problems. Reinforcement learning adjusts parameters by accumulating returns to meet the requirements of the environmental model. The proposed algorithm divides learners into superior and inferior groups based on fitness rankings and uses a reinforcement learning approach to dynamically adjust learner classification divisions to ensure adaptability in different optimization scenarios. In the teacher phase, superior learners emulate top performers, while inferior learners engage in guided mutual learning to enhance global search capabilities. In the learner phase, superior learners communicate with better peers, while inferior learners seek a wider range of information sources, balancing exploration and exploitation. In the experimental evaluation of five different photovoltaic models, the comparative analysis of eleven established algorithms verified its superior performance in accuracy, convergence speed, and complexity.

关键词： Photovoltaic system Parameter identification teaching-learning-based optimization Reinforcement learning

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teaching-learning-based optimization algorithm with dynamic neighborhood and crossover search mechanism for numerical optimization

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APPLIED SOFT COMPUTING 2024年 154卷

作者： Zeng, Zhibo Dong, He Xu, Yunlang Zhang, Wei Yu, Hangcheng Li, Xiaoping Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China China Univ Geosci Sch Mech & Elect Informat Wuhan 430074 Peoples R China Fudan Univ Sch Microelect Shanghai 200433 Peoples R China

This paper presents an improving teaching -learning -based optimization algorithm (called DRCMTLBO) combined with the dynamic ring neighborhood topology. Firstly, based on the individuals' fitness distribution and clustered state within and beyond the ring neighborhood, two evaluations of relative neighborhood quality (RNQ) are developed to separately guide population evolution. On the one hand, a dynamic neighborhood strategy driven by fitness -based evaluation is used to adjust neighborhood radius, maintaining individual variability and neighborhood diversity. On the other hand, to utilize individuals' information of the entire topology, a novel crossover search mechanism driven by Euclidean distance -based evaluation is used to expand the search space, determining whether individuals should enhance exploitation within the neighborhood or exploration beyond the neighborhood. Finally, the above strategies are embedded into the TLBO algorithm, assisted by improved search approaches that achieve a significant balance between exploitation and exploration. Numerical computation results on functions of CEC2014 and CEC2020 show that our proposed DRCMTLBO algorithm outperforms other ten typical algorithms significantly, and its computational performance can compete with several CEC winner algorithms.

关键词： Crossover search mechanism Ring neighborhood topology Relative neighborhood quality teaching-learning-based optimization algorithm

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Self-adaptive teaching-learning-based optimization with Reusing Successful learning Experience for Parameter Extraction in Photovoltaic Models

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第1期32卷 319-329页

作者： Du, Yang Ning, Bin Hu, Xiaowang Cai, Bojun Hubei Univ Arts & Sci Sch Automot & Traff Engn Hubei Key Lab Power Syst Design & Test Elect Vehic 296 Longzhong Rd Xiangyang 441053 Hubei Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Hubei Key Lab Power Syst Design & Test Elect Vehic 296 Longzhong Rd Xiangyang 441053 Hubei Peoples R China Hubei Univ Arts & Sci Sch Comp Engn 296 Longzhong Rd Xiangyang 441053 Hubei Peoples R China

This paper proposes a self-adaptive teaching-learning-based optimization with reusing successful learning experience (RSTLBO) to accurately and reliably extract parameters of different photovoltaic (PV) models. The key novelties of RSTLBO are: 1) Learners adaptively choose teacher or learner phase based on a selection probability according to their performance, balancing exploration and exploitation;2) Successful learner experiences are reused to enhance search capability. Experiments on single diode, double diode and PV panel models demonstrate that RSTLBO achieves higher accuracy and faster convergence than state-of-the-art methods like P-DE, TLBO, GOTLBO, etc. Specifically, RSTLBO obtains the minimum RMSE across all models, outperforms compared methods in statistical results, and exhibits fastest convergence in almost all cases. The self-adaptive probability selection and experience reuse make RSTLBO effective for PV parameter extraction.

关键词： learning experience optimization parameter extraction photovoltaic model teaching-learning-based optimization

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An Elite-Class teaching-learning-based optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

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Computers, Materials & Continua 2024年第4期79卷 47-63页

作者： Deming Lei Surui Duan Mingbo Li Jing Wang College of Automation Wuhan University of TechnologyWuhan430070China

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling *** this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion *** produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are *** teacher phase is composed of teacher competition and teacher *** learner phase is replaced with a reinforcement search of the elite *** adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in *** experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

关键词： Hybrid flow shop scheduling reentrant bottleneck stage teaching-learning-based optimization

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Optimizing Multi-Row Layouts With the Through-Aisle Structure: A Hybrid Approach of teaching-learning-based optimization and Linear Programming

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4972-4987页

作者： Zhang, Zeqiang Zhang, Yu Chen, Feng Liu, Junqi Wang, Can Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Chengdu SIWI High Tech Ind Co Ltd Chengdu 610031 Peoples R China

The multi-row facility layout problem is a prevalent and significant planning challenge in manufacturing workshops. This problem requires distributing facilities with pairwise transport weights among several rows to attain a layout with minimal logistics costs. However, the significance of aisles in multi-row facility layout has frequently been overlooked. An efficient aisle structure can result in a smooth transportation path and reduced material-handling costs. This paper contributes to the existing literature by introducing a new multi-row facility layout problem that considers long-straight aisles. First, mathematical formulas for the actual transportation distance between facilities through aisles are defined, and a mixed-integer programming model is constructed. Second, a hybrid algorithm based on an intelligent algorithm and a mathematical model is proposed. This method utilizes an improved teaching-learning-based optimization algorithm as a framework for optimizing the discrete facility sequence, and two decoding methods based on linear programming are designed to obtain the facility locations and transportation paths. Experimental results demonstrate that the two decoding strategies have their own advantages in terms of solution quality, efficiency, and area utilization. Moreover, improvement strategies for teaching-learning-based optimization algorithms are observed to be effective. Finally, we present two actual workshop examples of multi-row layout designs. The comparison of different algorithms reveals that the proposed algorithm has significant advantages in terms of solution quality and stability.

关键词： Layout Transportation Logistics Costs Mathematical models Conferences optimization Multi-row facility layout through-aisle structure mixed-integer linear programming teaching-learning-based optimization

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teaching-learning-based optimization of ring and rotor spinning processes

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SOFT COMPUTING 2021年第15期25卷 10287-10307页

作者： Diyaley, Sunny Chakraborty, Shankar Sikkim Manipal Inst Technol Dept Mech Engn Majitar Sikkim India Jadavpur Univ Dept Prod Engn Kolkata India

In a textile mill, ring and rotor spinning processes are the main technologies employed to convert cotton fibres into yarns with desirable qualities. It has been observed that in order to produce yarns having satisfactory quality characteristics, those spinning processes need to be run while setting different input variables (control parameters) at their optimal operating levels. In this paper, the optimal parametric combinations of back and front zone variables in a ring frame, and input variables in a rotor spinning process are determined based on teaching-learning-based optimization algorithm. The optimization performance of this algorithm is also compared with four other techniques, e.g., firefly algorithm, differential evolution algorithm, cuckoo search algorithm and quantum particle swarm optimization algorithm with respect to accuracy and consistency of the derived solutions. This optimization technique can thus be effectively applied to any of the intermediate processes in a textile mill to obtain the best combinations of different input variables so as to achieve the target quality characteristics.

关键词： Ring spinning Rotor spinning optimization teaching-learning-based optimization Yarn Quality

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A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Jing Li, Debiao Tang, Hongtao Li, Xixing Lei, Deming Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan 430068 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.

关键词： Fabric dyeing process Fuzzy energy-efficient scheduling Batch processing machine teaching-learning-based optimization

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Prediction of rock drillability using gray wolf optimization and teaching-learning-based optimization techniques

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SOFT COMPUTING 2024年第1期28卷 461-476页

作者： Fattahi, Hadi Ghaedi, Hossein Malekmahmoodi, Farshad Arak Univ Technol Fac Earth Sci Engn Arak Iran

An important index to evaluate the rock drilling ability in mines, tunnel drilling and underground drilling is the drilling rate index (DRI). Due to the complexity and nonlinearity of mechanical and physical properties of rocks, there are many uncertainties in DRI evaluation. For this reason, teaching-learning-based optimization (TLBO) and gray wolf optimization (GWO) have been used to consider uncertainties and establish a precise nonlinear relationship in the estimation of the DRI. In this study, 32 different rock types included metamorphic, igneous and sedimentary rocks were investigated in the laboratory to investigate the relationships between the DRI and input parameters. The modeling results show that the relationships determined for estimating the DRI by TLBO and GWO algorithms are accurate and close to the real value. It can also be concluded that the use of optimization algorithms to predict the DRI is very efficient.

关键词： Drilling rate index Prediction teaching-learning-based optimization Gray wolf optimization

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Hybrid modified marine predators algorithm with teaching-learning-based optimization for global optimization and abrupt motion tracking

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 19793-19828页

作者： Gao, Zeng Zhuang, Yi Chen, Chen Wang, Qiuhong Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

Marine predators algorithm (MPA) has solved many challenging optimization problems since proposed. However, corresponding to specific optimization tasks (e.g., visual tracking), it is usually hard to select correct multiple parameters in MPA, which will greatly limit the exploitation and exploration performance. As a result, MPA could be misled to a local minima or even did not converge. To solve this issue, we advise an enhanced version of MPA based on teaching-learning-based optimization (MMPA-TLBO) which can concurrently improve the solution accuracy and the convergence speed. Specifically, first, we propose a modified MPA (MMPA) that leverages chaotic map and opposition-based learning strategy in the initialization stage to generate high-quality individuals. Second, we introduce a parameter-free teaching-learning-based optimization method with strong exploitation operator into MPA, called MMPA-TLBO, which effectively trade-off between the exploitation and exploration procedures. Finally, extensive experiments over 23 benchmark functions, CEC2017 benchmark problems and two engineering design problems show that MMPA-TLBO is better than other algorithms. Furthermore, we perform a thought-provoking case study of MMPA-TLBO on visual tracking. The experimental results show that the MMPA-TLBO tracker can outperform other trackers with a satisfied margin, especially for abrupt motion tracking.

关键词： Marine predators algorithm teaching-learning-based optimization Benchmark functions Engineering design problems Visual tracking

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