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A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年 225卷 113474-113474页

作者： Li, Shuijia Gong, Wenyin Wang, Ling Yan, Xuesong Hu, Chengyu China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Photovoltaic (PV) systems play an important role in today's power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching-learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners' ranking probability is presented to adaptively choose the teacher or learner phase of TLBO;ii) based on the learners' ranking probability, an enhanced teaching manner with an adaptive teaching factor TF is proposed to make use of the exploitation abilities of better learners in the teacher phase;iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems.

关键词： Parameter identification Photovoltaic models teaching-learning-based optimization Differential evolution

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An ensemble multi-swarm teaching-learning-based optimization algorithm for function optimization and image segmentation

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Jiang, Ziqi Zou, Feng Chen, Debao Cao, Siyu Liu, Hui Guo, Wei Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

Intelligent optimization algorithms are widely utilized to deal with complex optimization problems in various areas. However, a single intelligent optimization algorithm cannot handle well more and more complex optimization problems. The ensemble strategy can integrate several different operators and algorithms by using some appropriate strategies and maybe obtain better optimization performance. In this paper, a new ensemble multi-swarm method based on teaching-learning-based optimization (EMTLBO) was proposed by integrating three different algorithms including the original teaching-learning-based optimization algorithm, its variant with neighborhood search and the variant with differential evolution. In EMTLBO, a new evaluating mechanism based on the fitness-based and diversity-based metrics (FDEM) for each sub-swarm was proposed to evaluate the optimization performance after a continuous generation interval. Moreover, an algorithm matching mechanism based on ranking for sub-swarms (AMM) is adapted to re-divide the population into three sub -swarms and match a suitable algorithm for each sub-swarm so as to increase the whole optimization performance. Furthermore, the experimental results on CEC2014 and CEC2017 test suits verify the feasibility and optimization performance of EMTLBO. Finally, the proposed algorithm is extended to optimize the segmentation thresholds of images and the segmentation performances on different benchmark images show that EMTLBO has good performance in most cases.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Ensemble strategy teaching-learning-based optimization Function optimization Image segmentation

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Optimal equivalent circuit models for photovoltaic cells and modules using multi-source guided teaching-learning-based optimization

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AIN SHAMS ENGINEERING JOURNAL 2024年第11期15卷

作者： Li, Yasha Xiong, Guojiang Mirjalili, Seyedali Mohamed, Ali Wagdy Guizhou Univ Coll Elect Engn Room 402Jiaxiu South Rd Guiyang 550025 Guizhou Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Univ Sci & Technol Sch Business Zewail City Sci & Technol Giza 12588 Egypt

The complexity of equivalent circuit models of photovoltaic cells and modules poses a difficult task to the parameter extraction methods. teaching-learning-based optimization (TLBO) is a potent metaheuristic-based parameter extraction method, but it suffers from insufficient precision and low dependability. This study presented a multi-source guided TLBO through improving its two optimization phases. A multi-source guided approach with one-to-one and step-by-step teaching strategies was designed to guide different learners in the teacher phase. Besides, different strategies based on multiple learners were introduced for learners with different knowledge reserves to strengthen information exchanging. With the improvements, it is advantageous to lessen the likelihood of hitting a local optimum and thereby the global convergence can be accelerated. The resultant method was verified on single diode model, double diode model, and three additional modules. The findings demonstrate that it obtained better solutions in precision and dependability, and stood out from the crowd of algorithms.

关键词： Metaheuristic algorithm Parameter extraction Photovoltaic cell teaching-learning-based optimization

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A multi-objective elitist feedback teaching-learning-based optimization algorithm and its application

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期188卷 115972-115972页

作者： Ren, Ziwu Jiang, Ruiqing Yang, Fan Qiu, Jie Soochow Univ Sch Mech & Elect Engn Suzhou 215131 Jiangsu Peoples R China

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching-learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching-learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms.

关键词： Multi-objective optimization teaching-learning-based optimization Feedback phase Non-dominated sorting Elitism strategy

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Hybridizing teaching-learning-based optimization With Adaptive Grasshopper optimization Algorithm for Abrupt Motion Tracking

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IEEE ACCESS 2019年 7卷 168575-168592页

作者： Zhang, Huanlong Gao, Zeng Ma, Xiaoyang Zhang, Jie Zhang, Jianwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Software Engn Coll Zhengzhou 450002 Peoples R China

Aiming at the problem that conventional tracking algorithms are difficult to deal with abrupt motion efficiently, an optimization algorithm called hybrid teaching-learning-based optimization with Adaptive Grasshopper optimization Algorithm (TLGOA) is proposed in this paper. Firstly, the non-linear strategy based on tangent function is used to replace the linear mechanism in the standard Grasshopper optimization Algorithm (GOA). The improved adaptive GOA (AGOA) can avoid the local trapping problem and enhance the global optimization ability, which can handle the problem of abrupt motion. Secondly, considering that teaching-learning-based optimization (TLBO) has obviously local exploitation operator and fast convergence, a hybrid TLGOA tracker is designed by combining the advantages of both AGOA and TLBO. The approach can enable better tracking accuracy and efficiency. Finally, extensive experimental results show that the proposed algorithm has obvious advantages over other algorithms, and also prove that TLGOA tracker is very competitive compared to other state-of-the-art trackers, especially for abrupt motion tracking.

关键词： Visual tracking abrupt motion grasshopper optimization algorithm teaching-learning-based optimization

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A multi-class teaching-learning-based optimization for multi-objective distributed hybrid flow shop scheduling

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KNOWLEDGE-based SYSTEMS 2023年第1期263卷

作者： Lei, Deming Su, Bin Wuhan Univ Technol Sch Automation Wuhan Peoples R China

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching-learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP. (c) 2023 Published by Elsevier B.V.

关键词： Hybrid flow shop Distributed scheduling Multiple classes teaching-learning-based optimization Reward and punishment mechanism

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A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Zhang, Jian Li, Muxi Yue, Xinxin Wang, Xiaojuan Shi, Maolin Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Anhui Sci & Technol Univ Coll Architecture Bengbu 233030 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

关键词： Hybrid optimization algorithm Differential evolution teaching-learning-based optimization Surrogate model High -dimensional expensive problems

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A stigmergic approach to teaching-learning-based optimization for continuous domains

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Meghdadi, Aghdas Akbarzadeh-T, M. R. Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Dept Elect Engn Mashhad Razavi Khorasan Iran

teaching-learning-based optimization (TLBO) has shown considerable success in solving complex optimization problems by imitating the higher cognitive leader-follower interactions among students and teachers in a classroom. Here, we propose to solve function optimization with a stigmergic probabilistic memory structure based on the indirect cooperation and distributed information exchange among students of a class along with a diversity preserving operator. The stigmergic approach integrates the historical experience, while the probabilistic representation and the diversity preserving operators avoid premature convergence. The resulting Stigmergic Real domain TLBO (SRTLBO) is applied to optimization problems with continuous variables. The proposed SRTLBO is evaluated on 40 standard benchmark functions, as well as in modeling the earthquake source and wave propagation in the East of Iran from actual data. Results indicate overall improved solutions of the SRTLBO algorithm when compared to several competing algorithms. In several cases, particularly those functions with a simpler structure, considerable improvement is also observed in the rate of convergence to the optimum solution. Superscript/Subscript Available

关键词： Metaheuristic optimization algorithms Swarm intelligence Stigmergy teaching-learning-based optimization Earthquake ground motion

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A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Yu, Xiaobing Zhang, Wen 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 210044 Peoples R China Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Sch Management Sci & Engn Nanjing 210044 Peoples R China

As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

关键词： teaching-learning-based optimization Reinforcement learning Wind farm layout optimization Q-learning algorithm Meta-heuristic algorithm

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Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved teaching-learning-based optimization

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ENERGIES 2016年第12期9卷 986页

作者： Chao, Kuei-Hsiang Wu, Meng-Cheng Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

The present study proposes a maximum power point tracking (MPPT) method in which improved teaching-learning-based optimization (I-TLBO) is applied to perform global MPPT of photovoltaic (PV) module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO.

关键词： maximum power point tracking teaching-learning-based optimization photovoltaic module array partial module shading

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