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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Kwan, Hon Keung Zhang, Miao Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781509028603

In this paper, teaching-learning-based optimization (TLBO) is used for minimax design of linear phase finite impulse response (FIR) digital Hilbert transformers. TLBO is a population-based and heuristic search algorithm which is parameter-free and exhibits a strong convergence ability. The results obtained from using TLBO to design Type 4 linear phase FIR highpass digital Hilbert transformers indicate that it is an effective method to reach smaller equiripple peak errors as compared to those obtained by the least-squares error minimization method and the optimal Parks-McClellan algorithm.

关键词： Digital Hilbert transformer Highpass digital Hilbert transformer design Type 4 linear phase FIR digital filters Minimax design teaching-learning-based optimization Evolutionary optimization Global optimization

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Hybrid teaching-learning based optimization with Harmony Search for Engineering optimization Problems 36

Hybrid Teaching-Learning Based Optimization with Harmony Sea...

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第36届中国控制会议

作者： Haibin Ouyang Ge Ma Guiyun Liu Zhifu Li Xiaojing Zhong School of Mechanical and Electric Engineering Guangzhou University

ISBN: (纸本)9781538629185

In order to improve the performance of teaching-learning-based optimization algorithm, a hybrid teaching-learning-based optimization algorithm is presented in this paper. The hybrid approach combine with the strength of harmony search algorithm and teaching-learning-based optimization algorithm, which is aim to enhance the global search ability and local exploitation cababillity. Moreover, A new learning operation is used in learning phase for improving learning efficiency. Several classic engineering cases are selected to evaluate the performance of the proposed algorithm. Results reveal that the proposed algorithm outperforms TLBO and some other promising approachs.

关键词： teaching-learning-based optimization harmony search algorithm learning operation

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Modified teaching-learning based optimization for 0-1 knapsack optimization Problems 29

Modified Teaching-Learning Based Optimization for 0-1 knapsa...

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29th Chinese Control And Decision Conference (CCDC)

作者： Ouyang, Haibin Wang, Qing Kong, Xiangyong Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China

ISBN: (纸本)9781509046577

In this article, a modified teaching-learning-based optimization (MTLBO) algorithm is proposed for solving 0-1 knapsack optimization problems. The MTLBO incorporated estimation of distribution operation into teaching phases, which aims at reducing the possibility of premature and predicting an elite teacher. A stochastic local exploitation is used in teaching phase for improving local searching capability. Moreover, in the learning phase, a new global learning operation is presented to boost learning efficiency. Several classic 0-1 knapsack cases are selected to evaluate the performance of MTLBO. Numerical results reveal that the proposed algorithm surpasses TLBO and several other promising heuristic methods.

关键词： teaching-learning-based optimization 0-1 knapsack optimization problems estimation of distribution operation global learning operation

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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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Synthesis of thinned planar antenna arrays using teaching-learning-based optimization

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2015年第5期7卷 557-563页

作者： Dib, Nihad I. Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching-learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

关键词： Antenna arrays optimization techniques teaching-learning-based optimization

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Diagnosis of Multiple Wiring Faults Using Time-Domain Reflectometry and teaching-learning-based optimization

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ELECTROMAGNETICS 2015年第1期35卷 10-24页

作者： Boudjefdjouf, H. Mehasni, R. Orlandi, A. Bouchekara, H. R. E. H. De Paulis, F. Smail, M. K. Univ Constantine 1 Dept Elect Engn Elect Engn Lab Constantine Constantine 25000 Algeria Univ Aquila Dept Ind & Informat Engn & Econ Electromagnet Compatibil Lab I-67100 Laquila Italy Univ Paris Est IFSTTAR F-77747 Champs Sur Marne France

Time-domain reflectometry has proven to be one of the best methods for wiring network diagnosis, and it can easily be applied to the detection and localization of defects, while only requiring one access point to the wiring network. In this article, a novel approach combining the time-domain reflectometry response extracted from vector network analyzer measurements and the teaching-learning-based optimization technique is developed and applied to the diagnosis of wiring networks. The proposed approach consists of two steps. In the first step, propagation along the cables is modeled using the forward model. In the second step, teaching-learning-based optimization is used to solve the inverse problem to deduce physical information about the defects in the wiring network. The proposed approach has been successfully tested on several cases and for different configurations. Comparisons of the proposed time-domain reflectometry/teaching-learning-based optimization approach results with measurements reveal that this approach has a high potential and is very effective for wiring network diagnosis.

关键词： finite-difference time-domain method multiple wiring diagnosis time-domain reflectometry teaching-learning-based optimization

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An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time

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NEUROCOMPUTING 2015年 148卷 260-268页

作者： Xu, Ye Wang, Ling Wang, Sheng-yao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 10084 Peoples R China

In this paper, an effective teaching-learning-based optimization algorithm (TLBO) is proposed to solve the flexible job-shop problem with fuzzy processing time (FJSPF). First, a special encoding scheme is used to represent solutions, and a decoding method is employed to transfer a solution to a feasible schedule in the fuzzy sense. Second, a bi-phase crossover scheme based on the teaching-learning mechanism and special local search operators are incorporated into the search framework of the TLBO to balance the exploration and exploitation capabilities. Moreover, the influence of the key parameters on the TLBO is investigated using the Taguchi method. Finally, numerical results based on some benchmark instances and the comparisons with some existing algorithms are provided. The comparative results demonstrate the effectiveness and efficiency of the proposed TLBO algorithm in solving the FJSPF. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Flexible job-shop scheduling problem Fuzzy processing time teaching-learning-based optimization Taguchi method

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A multi-objective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint

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JOURNAL OF CLEANER PRODUCTION 2015年第Aug.15期101卷 337-347页

作者： Lin, Wenwen Yu, D. Y. Zhang, Chaoyong Liu, Xun Zhang, Sanqiang Tian, Yuhui Liu, Shengqiang Xie, Zhanpeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Univ Michigan Dept Mech Engn SMWu Mfg Res Ctr Ann Arbor MI 48109 USA Shan Dong Hoteam Software Co Ltd Jinan Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan Peoples R China

Industry is responsible for nearly half of the global energy consumption. Recent studies on sustainable manufacturing focused on energy saving to reduce the unit production cost and environmental impacts. Besides energy consumption, certain manufacturing activities in machine shops, such as the use of cutting fluids, disposal of worn tools, and material consumption, also cause other environmental impacts. Since all these activities lead to carbon footprint directly or indirectly, carbon footprint can be employed as a new and overall environment criterion in manufacturing. In this study, an integrated model for processing parameter optimization and flow-shop scheduling was developed. Objectives to minimize both makespan and carbon footprint were considered simultaneously, which was solved by a multi-objective teaching learning-based optimization algorithm. Furthermore, three carbon-footprint-reduction strategies were employed to optimize the scheduling results: (i) postponing strategy, (ii) setup strategy, and (iii) processing parameter preliminary optimization strategy. In the theoretical aspect, the strategies greatly improved the performance of the optimization results through reducing machine idle time and cutting down the search space. From the perspective of practical applications, these strategies greatly help elevate production efficiency and reduce environmental impacts. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Processing parameter optimization Flow-shop scheduling teaching-learning-based optimization Carbon footprint Sustainable manufacturing

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Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 146卷 198-210页

作者： Yu, Kunjie Wang, Xin Wang, Zhenlei E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

A self-adaptive multi-objective teaching-learning-based optimization (SA-MTLBO) is proposed in this paper. In SA-MTLBO, the learners can self-adaptively select the modes of learning according to their levels of knowledge in classroom. The excellent learners are more likely to choose the learner phase to enhance population diversity, and the common learners are tend to choose the teacher phase to improve the convergence ability of the algorithm. So learners at different levels choose appropriate modes of learning and carry out corresponding search function to efficiently enhance the performance of algorithm. To evaluate the effectiveness of the proposed algorithm, SA-MTLBO is firstly compared with other algorithms in twelve test problems. The results demonstrate that SA-MTLBO can generate Pareto optimal fronts with good convergence and distribution. Finally, SA-MTLBO is used to maximize the yields of ethylene, propylene, and butadiene of the naphtha pyrolysis process. The computational results of SA-MTLBO indicate that the operation of ethylene cracking furnace can be improved by increasing the yields of ethylene, propylene, and butadiene. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization teaching-learning-based optimization Cracking furnace Operation optimization

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Modified teaching-learning based optimization for 0-1 knapsack optimization Problems

Modified Teaching-Learning Based Optimization for 0-1 knapsa...

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第29届中国控制与决策会议

作者： Haibin OUYANG Qing WANG Xiangyong KONG School of Mechanical and Electric Engineering Guangzhou University School of Electrical Engineering and Automation Jiangsu Normal University

ISBN: (纸本)9781509046584

In this article,a modified teaching-learning-based optimization(MTLBO) algorithm is proposed for solving0-1 knapsack optimization *** MTLBO incorporated estimation of distribution operation into teaching phases,which aims at reducing the possibility of premature and predicting an elite teacher.A stochastic local exploitation is used in teaching phase for improving local searching ***,in the learning phase,a new global learning operation is presented to boost learning *** classic 0-1 knapsack cases are selected to evaluate the performance of *** results reveal that the proposed algorithm surpasses TLBO and several other promising heuristic methods.

关键词： teaching-learning-based optimization 0-1 knapsack optimization problems estimation of distribution operation global learning operation

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