检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

PARAMETER IDENTIFICATION APPROACH USING IMPROVED teaching AND learning based optimization FOR HUB MOTOR CONSIDERING TEMPERATURE RISE

引用

METROLOGY AND MEASUREMENT SYSTEMS 2023年第1期30卷 99-115页

作者： Li, Yong Wang, Juan Zhang, Taohua Hu, Han Wu, Hao Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Beijing Inst Space Launch Technol Beijing 100076 Peoples R China

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance,d -axis inductance, d-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, d-axis inductance, d-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified.

关键词： parameters identification teaching-learning-based optimization hub motor temperature rise

来源：评论

学校读者我要写书评

暂无评论

Modified teaching-learning-based optimization and applications in multi-response machining processes

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 174卷

作者： Ang, Koon Meng Natarajan, Elango Sharma, Abhishek Rahman, Hameedur Then, Richie Yi Shiun Alrifaey, Moath Tiang, Sew Sun Lim, Wei Hong Isa, Nor Ashidi Mat UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus George Town 14300 Malaysia Ariel Univ Dept Elect & Elect Engn IL-40700 Ariel Israel Univ Petr & Energy Studies Dept Elect & Elect Engn Dehra Dun 248007 India Air Univ Fac Comp & Artificial Intelligence Islamabad Capital Territo 44000 Pakistan

Many real-world engineering problems such as machining processes are multi-objective optimization problems (MOPs) because multiple performance characteristics are considered to satisfy their contradictory goals. An improved multi-objective teaching-learning-based optimization with refined knowledge sharing mechanisms (IMTLBO-RKSM) is proposed to tackle these MOPs effectively. Pareto dominance concept is first incorporated into IMTLBO-RKSM to handle the tradeoffs of multiple contradictory objectives. Appropriate modifications are incorporated into both teacher and learner phases of IMTLBO-RKSM to emulate to emulate the knowledge sharing processes of classroom more accurately, hence achieving better balancing of exploration and exploitation searches. Particularly, both concepts of Euclidean-distance based teacher assignment scheme and social learning are incorporated into the IMTLBO-RKSM's teacher phase to derive the unique directional information that can provide better guidance for each learner. The learner phase of IMTLBO-RKSM is also modified by designing two new learning mechanisms known as independent learning and adaptive peer learning, aiming to facilitate different preferences of learners in acquiring new knowledge. The performance of IMTLBO-RKSM is evaluated and compared with six multi-objective optimization methods by using five case studies of multi-response machining problems and twelve MOP benchmark functions. Extensive simulation studies show that IMTLBORKSM have more competitive performance than other methods by generating Pareto fronts with better quality in terms of accuracy and diversity of solution members for most tested problems.

关键词： Multi-objective optimization Multi-response machining Pareto dominance teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

Parameters Extraction of Photovoltaic Models Using Triple-Phase teaching-learning-based optimization

引用

IEEE ACCESS 2020年 8卷 69937-69952页

作者： Liao, Zuowen Chen, Zhikun Li, Shuijia Beibu Gulf Univ Coll Econ & Management Beibu Gulf Big Data Resources Utilisat Lab Qinzhou 535011 Peoples R China Beibu Gulf Univ Coll Resources & Environm Beibu Gulf Big Data Resources Utilisat Lab Guangxi Key Lab Marine Disaster Beibu Gulf Qinzhou 535011 Peoples R China China Univ Geosci Dept Comp Sci Wuhan 430074 Peoples R China

Parameters extraction of photovoltaic (PV) models is urgently desired for the simulation, control, and evaluation of PV systems. To accurately and reliably extract the parameters of different PV models, a triple-phase teaching-learning-based optimization (TPTLBO) is proposed in this paper. The novelty of TPTLBO lies in: i) teaching-learning-based optimization introduces a buffer phase and adopts a centroid strategy to update the position of intermediate learners, which further strengthens the exploration and exploitation;ii) the learners can select different phases and employ different learning strategies based on their knowledge level;iii) a dynamic control parameter replaces the original random parameter rand to enhance the search ability of algorithm. The parameters extraction performance of TPTLBO is verified through the single diode model, the double diode model, and three PV models. Experimental results demonstrate that TPTLBO achieves better performance in terms of accuracy and reliability compared to state-of-the-art algorithms.

关键词： Photovoltaic models parameter extraction teaching-learning-based optimization triple-phase

来源：评论

学校读者我要写书评

暂无评论

Dynamic opposite learning enhanced teaching-learning-based optimization

引用

KNOWLEDGE-based SYSTEMS 2020年 188卷 104966-104966页

作者： Xu, Yunlang Yang, Zhile Li, Xiaoping Kang, Huazhou Yang, Xiaofeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Fudan Univ Sch Microelect State Key Lab ASIC & Syst Shanghai 200433 Peoples R China

The teaching-learning-based optimization (TLBO) algorithm has been one of most popular bio-inspired meta-heuristic algorithms due to the competitive converging speed and high accuracy. A batch of TLBO variants has been proposed to enhance the exploitation ability and accelerate the exploration process. However, they still suffer from premature convergence in solving complex non-linear problems. In the study, a novel TLBO variant named dynamic-opposite learning TLBO (DOLTLBO) is proposed, which employs a new dynamic-opposite learning (DOL) strategy to overcome premature convergence. The search space of DOL has the characteristics of asymmetry and dynamically adjusting along with a random opposite number. The asymmetric search space significantly increase the probability for the population in obtaining the global optimum, which holistically improves the exploitation capability of DOLTLBO. Meanwhile, the dynamically changing characteristic enriches the diversity of the search space, thus enhancing the exploration ability. To validate the proposed DOL operator and DOLTLBO algorithm, shifted and rotated benchmark functions from CEC 2014, multiextremal functions and constrained engineering problems have been experimented upon. Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： teaching-learning-based optimization Dynamic-opposite learning Opposition-based learning Global optimization

来源：评论

学校读者我要写书评

暂无评论

Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching-learning-based optimization

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第6期234卷 1146-1155页

作者： Venu, Borigorla Raju, L. Suvarna Rao, K. Venkata Vignans Fdn Sci Technol & Res Univ Dept Mech Engn Guntur 522213 Andhra Pradesh India

This study focuses on optimization of process parameters, which may result in improved mechanical properties of the friction stir weldments of AA2014-T651. Plain taper and threaded taper cylindrical tool pin profiles were used for the study. A set of experiments was conducted at different levels of tool rotational and weld speeds using two tool pin profiles. Mechanical properties such as tensile strength, yield strength, impact strength, percentage of elongation, and hardness were measured. Objective functions are developed for the five mechanical properties in terms of input parameters. The input parameters were optimized using teaching-learning-based optimization algorithm technique to improve mechanical properties. The teaching-learning-based optimization algorithm suggested three best combinations such as combination-I (940 r/min and 32 mm/min), combination-II (1100 r/min and 40 mm/min), and combination-III (1205 r/min and 45 mm/min). The optimization is also validated with experimental results.

关键词： Friction stir welding rotational speed weld speed teaching-learning-based optimization mechanical properties mathematical models multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Modeling and simulation to optimize direct power control of DFIG in variable-speed pumped-storage power plant using teaching-learning-based optimization technique

引用

SOFT COMPUTING 2020年第22期24卷 16895-16915页

作者： Hosseini, Seyed Mohammad Hassan Rezvani, Alireza Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran Islamic Azad Univ Saveh Branch Dept Elect Engn Tehran Iran

The issues related to the optimal control of large-scale storage systems in electric power systems such as pumped storage (PS) plant have turned into vital challenges in the way of integrating renewable energy sources into power systems to provide reliable and economical electric energy. In this regard, this paper uses the direct power control strategy to model and simulate a variable-speed PS plant, which includes a doubly fed induction generator (DFIG). The active and the reactive power of the stator would be able to be controlled, separately. This approach has a better dynamic performance compared to other methods, while it would be quite simple to implement. But there are some shortfalls with this method, such as high ripple relating to the active power as well as reactive power together with the current harmonics. In this respect, the space vector modulation (SVM) is applied to eliminate these shortfalls. In the proposed control technique, including SVM, the dynamic performance of the studied DFIG unit is controlled using the proportional-integral (PI) controller. It should be noted that the teaching-learning-based optimization (TLBO) method is employed to tune the PI controller for controlling the DFIG system in the PS plant. Finally, in order to validate the performance of the suggested framework, a comparison is made between the results obtained by the TLBO and the ones reported by other optimization methods. The obtained results using the TLBO algorithm indicate better performance of the PI controller to reduce the ripples of the active and reactive power of the stator as well as the harmonic power.

关键词： Pumped-storage power plant Doubly fed induction generator teaching-learning-based optimization Imperialist competitive algorithm Particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Techno-economic analysis of a grid-connected PV/battery system using the teaching-learning-based optimization algorithm

引用

SOLAR ENERGY 2020年 203卷 69-82页

作者： Ashtiani, Mina Najafi Toopshekan, Ashkan Astaraei, Fatemeh Razi Yousefi, Hossein Maleki, Akbar Tamin Ehtiajat Fanni Tehran TAF Co Tehran Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehn Diode Idial Factoran Tehran Iran Shahrood Univ Technol Fac Mech Engn Shahrood Iran

In this paper, a novel framework for optimal sizing of a grid-connected photovoltaic (PV)/battery system is presented to minimize the total net present cost using a novel optimization algorithm based on the teaching and learning process, namely teaching-learning-based optimization (TLBO). The TLBO algorithm is an efficient optimization method based on the teacher's influence on the learners' output in a class. This article shows how backup PV/battery systems can reduce electricity bills, even in countries where their electricity is cheap and subsidized. In comparison to the non-renewable case, the net present cost (NPC) and the cost of energy (COE) of the on-grid PV/battery system are 15.6% and 16.8% more efficient, respectively. The NPC and COE factors were calculated and compared with two other popular optimization algorithms, particle swarm optimization, and genetic algorithm to validate the proposed approach and to ascertain the strength and accuracy of the TLBO algorithm. To compare the results, different cities were examined and the similarity of the results showed that the system is efficient regardless of the surrounding climate. Also, sensitivity analyses on different cities' climatic data, different load demands and PV prices outlined the economic optimal size of the system.

关键词： Sustainable energy Sizing PV-battery system Peak demand teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

An ameliorated teaching-learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur's entropy and Otsu's between class variance

引用

INFORMATION SCIENCES 2020年 533卷 72-107页

作者： Wu, Bo Zhou, Jianxin Ji, Xiaoyuan Yin, Yajun Shen, Xu Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

In this paper, multi-threshold image segmentation approaches using an improved teaching-learning-based optimization algorithm (DI-TLBO) are presented and the proposed DI-TLBO-based methods obtain satisfactory segmentation results. This work is presented as follows. Firstly, two random numbers are introduced to determine the learning methods of the learner in the teacher phases and the learner phases of DI-TLBO. Randomness of the learning methods further improves global optimization ability of DI-TLBO. Self-feedback learning phase and mutation-crossover phase are also introduced into DI-TLBO algorithm, which makes DI-TLBO achieve better exploration ability. The comparative results of DI-TLBO with other evolutionary algorithms (EAs) on a set of benchmarks functions demonstrate that DI-TLBO acquires better solution accuracy than other EAs. Then the proposed DI-TLBO algorithm is applied to solve multi-level threshold image segmentation problems modeled by Otsu's between class variance function and Kapur's entropy function. Experiments comparing DI-TLBO-based methods with other EAs based approaches on standard test images show that DI-TLBO-based methods possess superior performance in terms of both solution accuracy and stability of segmentation results. Finally, the proposed DI-TLBO-based methods are successfully applied in casting X-ray image segmentation for multi-level threshold. Although the defects in high resolution X-ray image (3072 x 2400) are easy to be ignored and omitted when being detected artificially, all the defects are segmented perfectly using the proposed DI-TLBO-based methods. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Multilevel thresholding Image segmentation teaching-learning-based optimization Otsu's threshold Kapur's entropy

来源：评论

学校读者我要写书评

暂无评论

A new hybrid optimization method combining moth-flame optimization and teaching-learning-based optimization algorithms for visual tracking

引用

SOFT COMPUTING 2020年第24期24卷 18321-18347页

作者： Reddy, K. Narsimha Bojja, Polaiah Koneru Lakshmaiah Educ Fdn ECE Dept Vaddeswaram 522502 Andhra Pradesh India

In this paper, a new hybrid algorithm based on moth-flame optimization (MFO) and teaching-learning-based optimization (TLBO) algorithm named as MFO-TLBO is proposed to overwhelm their shortcomings and inherit their advantages using the low-level coevolutionary mixed hybrid. In the best interests of this, we progress the competence of exploitation in TLBO with the ability of exploration in the MFO algorithm to demonstrate the metiers of both methods. The sole inspiration behind integrating modifications in MFO is to benefit the procedure to avoid immature convergence and to steer the search in the direction of the potential search region in a quicker way. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. The obtained results from basic and nonparametric statistical tests confirmed that this hybrid method dominates in terms of convergence and success rate. Furthermore, MFO-TLBO is applied to visual tracking as a real-life application. All experimental outcomes, illustrations and comparative investigation found that the MFO-TLBO algorithm can vigorously track a random target object in many stimulating circumstances than the other trackers successfully.

关键词： optimization Moth-flame optimization MFO-TLBO Visual tracking Population-based algorithm teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：