检索结果-内蒙古大学图书馆

Synthesis of Thinned Concentric Circular Antenna Arrays Using teaching-learning-based optimization

INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2014年第4期24卷 443-450页

作者： Dib, Nihad Sharaqa, Ashraf Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan WorleyParsons Arabia Ltd Commun & Secur Projects Div Al Khobar 31952 Saudi Arabia

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method;namely, the teaching-learning-based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. (C) 2013 Wiley Periodicals, Inc.

关键词： antenna arrays circular arrays optimization techniques teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

Effective hybridization of JAYA and teaching-learning-based optimization algorithms for numerical function optimization

引用

SOFT COMPUTING 2023年第14期27卷 9673-9691页

作者： Gholami, Jafar Nia, Fariba Abbasi Sanatifar, Maryam Zawbaa, Hossam M. Islamic Azad Univ Dept Comp Engn Kermanshah Sci & Res Branch Kermanshah Iran Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

The JAYA is classified as the state-of-the-art population-oriented algorithm for the optimization of diverse problems, both discrete and continuous. The concept behind this algorithm is to present a solution by means of the best and worst individuals in the population. On the other hand, teaching-learning-based optimization algorithm cooperation of a teacher on students' learning process. Due to each one having some benefits and drawbacks, combining those leads to better exploring the problem. Consequently, this investigation exploits the hybridization of both mentioned algorithms, and a novel algorithm is made named H-JTLBO (hybridization of JAYA and teaching learning-based optimization). The proposed approach is then evaluated using different test functions used frequently in the literate. Finally, the results of such functions are compared with other optimization algorithms which have recently been introduced in the literature, such as Sine Cosine Algorithm (SCA), Grasshopper optimization Algorithm (GOA), Moth-flame optimization (MFO), and JAYA algorithm. In addition, the statistical test is used to evaluate the proposed method. Through the results, H-JTLBO outperforms all mentioned algorithms in terms of convergence and solution quality.

关键词： JAYA teaching-learning-based optimization Hybridization of JAYA and teaching-learning-based optimization algorithms Convergence

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

Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks

引用

APPLIED INTELLIGENCE 2018年第9期48卷 2755-2769页

作者： El Ghazi, Asmae Ahiod, Belaid Mohammed V Univ Rabat LRIT Associated Unit CNRST URAC 29 Fac Sci Rabat Morocco

Wireless sensor networks (WSNs) are composed of sensor nodes, having limited energy resources and low processing capability. Accordingly, major challenges are involved in WSNs Routing. Thus, in many use cases, routing is considered as an NP-hard optimization problem. Many routing protocols are based on metaheuristics, such as Ant Colony optimization (ACO) and Particle Swarm optimization (PSO). Despite the fact that metaheuristics have provided elegant solutions, they still suffer from complexity concerns and difficulty of parameter tuning. In this paper, we propose a new routing approach based on teaching learning based optimization (TLBO) which is a recent and robust method, consisting on two essential phases: Teacher and Learner. As TLBO was proposed for continuous optimization problems, this work presents the first use of TLBO for the discrete problem of WSN routing. The approach is well founded theoretically as well as detailed algorithmically. Experimental results show that our approach allows obtaining lower energy consumption which leads to a better WSN lifetime. Our method is also compared to some typical routing methods;PSO approach, advanced ACO approach, Improved Harmony based approach (IHSBEER) and Ad-hoc On-demand Distance Vector (AODV) routing protocol, to illustrate TLBO's routing efficiency.

关键词： Wireless sensor network Metaheuristic Routing Ad-hoc on-demand distance vector Ant colony optimization Particle swarm optimization Harmony search teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid teaching-learning-based optimization for Workflow Scheduling in Cloud Environment

引用

IEEE ACCESS 2023年 11卷 100755-100768页

作者： He, Jieguang Liu, Xiaoli Guangdong Univ Petrochem Technol Coll Comp Sci Maoming 525011 Peoples R China

At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), teaching-learning-based optimization (TLBO), Opposition-based learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO's performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.

关键词： Workflow scheduling cloud computing teaching-learning-based optimization opposition-based learning search boundary population information

来源：评论

学校读者我要写书评

暂无评论

An improved teaching-learning-based optimization algorithm and itsapplication to a combinatorial optimization problem in foundry industry

引用

APPLIED SOFT COMPUTING 2017年 57卷 504-516页

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

teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability. (C) 2017 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Historical population Self-feedback learning Mutation and crossover Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

A note on teaching-learning-based optimization algorithm

引用

INFORMATION SCIENCES 2012年 212卷 79-93页

作者： Crepinsek, Matej Liu, Shih-Hsi Mernik, Luka Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA CALTECH Pasadena CA 91125 USA

teaching-learning-based optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO's dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step;(2) incorrect formulae on a number of fitness function evaluations;and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics' researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Constrained optimization problems Unconstrained optimization problems Experimental replication

来源：评论

学校读者我要写书评

暂无评论

optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第14期231卷 2596-2607页

作者： Dikshit, Mithilesh K. Puri, Asit B. Maity, Atanu Natl Inst Technol Durgapur Dept Mech Engn MG Ave Durgapur 713209 W Bengal India CSIR CMERI Adv Design & Optimizat Durgapur India

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth (f(z))=0.06mm, axial depth of cut (A(p))=0.74mm, cutting speed (V-c)=145.8m/min, and radial depth of cut (A(e))=0.38mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 mu m and has been validated by confirmation experiments.

关键词： Ball-end milling composite desirability function optimization response surface methodology surface roughness teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

A hybridization of teaching-learning-based optimization and differential evolution for chaotic time series prediction

引用

NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1407-1422页

作者： Wang, Lei Zou, Feng Hei, Xinhong Yang, Dongdong Chen, Debao Jiang, Qiaoyong Cao, Zijian Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching-learning-based optimization (TLBO)-differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm's performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO-DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.

关键词： Chaotic time series Hybrid optimization teaching-learning-based optimization Differential evolution

来源：评论

学校读者我要写书评

暂无评论

Competitive teaching-learning-based optimization for multimodal optimization problems

引用

SOFT COMPUTING 2022年第19期26卷 10163-10186页

作者： Chi, Aining Ma, Maode Zhang, Yiying Jin, Zhigang Taizhou Univ Coll Informat Engn Taizhou 225300 Peoples R China Qatar Univ Coll Engn Doha 2713 Qatar Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

teaching-learning-based optimization is one of the latest metaheuristic algorithms. TLBO has a simple framework and good global search ability. In addition, TLBO only needs population size and terminal condition for performing search tasks. Given these advantages, TLBO has been used widely since it was proposed. However, TLBO may fall into local optimal solutions in solving complex multimodal optimization problems. This paper reports an improved TLBO, namely competitive teaching-learning-based optimization, for solving multimodal optimization problems. In CTLBO, population is first divided into outstanding group and common group by the designed competitive mechanism. Then outstanding group is updated by the learning strategies of TLBO and common group is guided by outstanding group. In addition, a mutation operator for the optimal individual is introduced to increase the ability of CTLBO to escape from the local optima. The performance of CTLBO is investigated by 45 benchmark test functions from CEC 2014 and CEC 2015 test suites and three challenging real-world engineering problems. Experimental results show that CTLBO is more reliable and efficient on most test cases than TLBO and the other compared algorithms. This supports the effectiveness of the improved strategies and the superiority of CTLBO in solving multimodal optimization problems.

关键词： teaching-learning-based optimization Engineering optimization Multimodal problems Competitive learning mechanism

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：