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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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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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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: An optimization method for continuous non-linear large scale problems

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INFORMATION SCIENCES 2012年第1期183卷 1-15页

作者： Rao, R. V. Savsani, V. J. Vakharia, D. P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

An efficient optimization method called 'teaching-learning-based optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods. (C) 2011 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Non-linear optimization problems Benchmarks functions

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teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

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COMPUTER-AIDED DESIGN 2011年第3期43卷 303-315页

作者： Rao, R. V. Savsani, V. J. Vakharia, D. P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

A new efficient optimization method, called 'teaching-learning-based optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： teaching-learning-based optimization Constrained benchmark functions Mechanical design optimization

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teaching-learning-based optimization algorithm for multi-area economic dispatch

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ENERGY 2014年 68卷 21-28页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents teaching learning-based optimization algorithm for solving MAED (multi-area economic dispatch) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. TLBO (teaching learning-based optimization) is one of the recently proposed population based algorithms which simulates the teaching learning process of the class room. It is a very simple and robust global optimization technique. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-area economic dispatch Tie line constraints teaching-learning-based optimization

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teaching-learning-based optimization Enhanced With Multiobjective Sorting based and Cooperative learning

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IEEE ACCESS 2020年 8卷 65923-65937页

作者： Li, Wei Fan, Yaochi Xu, Qingzheng Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China

teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.

关键词： teaching-learning-based optimization non-dominated sorting cooperative learning optimization algorithm learning strategy

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teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem

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SOFT COMPUTING 2017年第6期21卷 1537-1548页

作者： Zheng, Huan-yu Wang, Ling Zheng, Xiao-long Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a teaching-learning-based optimization algorithm (TLBO) is proposed to solve the multi-skill resource constrained project scheduling problem (MS-RCPSP) with makespan minimization criterion. A task-resource list-based encoding scheme is presented by combining the task list and the resource list, and a left-shift decoding scheme is developed to generate feasible schedules. To achieve satisfactory performances, the balance between global exploration and local exploitation is stressed in designing the TLBO algorithm. At the initialization stage, a balanced resource rule is proposed to generate the initial resource lists, and multiple task list rules are adopted in a hybrid way to initialize the task lists. At the teacher phase and the student phase, the two-point crossover and the resource-based local search are utilized to generate the promising task-resource lists. Moreover, a reinforcement phase is incorporated into the original TLBO with both the permutation-based and the resource-based local search strategies as an additional phase to enhance the local intensification capability. To investigate the influence of parameter setting on the TLBO, numerical tests based on Taguchi method of design of experiment are carried out. In addition, the effectiveness of the proposed balanced resource rule is shown by statistical comparisons with the random resource rule. Computational comparisons between TLBO and the existing algorithm also demonstrate the effectiveness and efficiency of the proposed TLBO in solving the MS-RCPSP.

关键词： teaching-learning-based optimization Project scheduling Multi-skill Balanced resource rule Reinforcement phase

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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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teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

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ENGINEERING optimization 2012年第12期44卷 1447-1462页

作者： Rao, R. V. Savsani, V. J. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat Gujarat India Univ Maribor Fac Mech Engn Maribor Slovenia

An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, epsilon-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.

关键词： real-parameter optimization unconstrained benchmark functions constrained benchmark functions teaching-learning-based optimization

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