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61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Kwan, Hon Keung Zhang, Miao Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781538673928

In this paper, weighted least-squares (WLS) design of 2-dimensional nonlinear phase circularly symmetric finite impulse digital filters using teaching-learning-based optimization is presented. To improve the performance of the teaching-learning-based optimization algorithm, an elitist replacement with mutation is applied to the worst solution in a population. Three 2D nonlinear phase FIR lowpass digital filters are designed and the results indicate that lower mean squared complex errors can be obtained as compared to those obtained by another design method.

关键词： 2-D nonlinear phase FIR digital filter design Complex frequency response approximation WLS design teaching-learning-based optimization

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Power System Stability Enhancement with teaching-learning-based optimization 26

Power System Stability Enhancement with Teaching-Learning-Ba...

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Naghizadeh, Ramezan Ali Azimi, Seyed Mohammad Hamedan Univ Technol Elect Engn Dept Hamadan *** Iran

ISBN: (纸本)9781538649169

Modern electric power systems are complex, interconnected and susceptible to low frequency oscillations. Power system stabilizers (PSSs) are used in synchronous generators to damp out these oscillations and prevent instability in the case of different possible disturbances. This paper proposes application of an optimal approach for two different controller types to improve PSS performance and enhance power system stability. Three types of controllers including Lead-Lag, fuzzy, and sliding mode are considered for PSS of a single machine infinite bus power system. The parameters of fuzzy and sliding mode controllers are optimized using teaching-learning-based optimization (TLBO) algorithm to minimize oscillations. Simulation results show that TLBO algorithm is capable of proper tuning of different PSS controller strategies to not only maintain stability, but also to reduce oscillations. In addition, performance of different controllers is evaluated for the same specific disturbance. Significant improvement is observed in fuzzy logic controller. In addition, it has been found that SMC optimized by TLBO exhibit best results compared to other controllers.

关键词： component Power System Stabilizer Fuzzy Logic Control Sliding Mode Control teaching-learning-based optimization

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An improved teaching-learning-based optimization 37

An improved teaching-learning-based optimization

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37th Chinese Control Conference (CCC)

作者： Hou, Jie Ren, Ziwu Lu, Pan Zhang, Kunting Soochow Univ Robot & Microsyst Ctr Jiangsu Prov Key Lab Adv Robot Suzhou 215021 Peoples R China Soochow Univ Collaborat Innovat Ctr Suzhou Nano Sci & Technol Suzhou 215021 Peoples R China

ISBN: (纸本)9789881563958

teaching-learning-based optimization(TLBO) is a new proposed heuristic algorithm for optimization applications in recent years. In this paper, an improved TLBO algorithm (ITLBO) is presented. In the teacher phase, the second-teaching strategy and self-exploration study of teacher are introduced to improve the convergence speed. And the improved learner phase can ensure the diversity of the population to avoid the possibility of falling into a local optimum. Meanwhile, second-teaching strategy and the improved learner phase enable the algorithm to use fine local search and improve the precision. To assess the performance of ITLBO algorithm, experiments are implemented on 8 classical benchmark functions. The result show that ITLBO algorithm is an effective approach.

关键词： teaching-learning-based optimization Second-teaching strategy Teacher's self-exploration study

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ABC-TLBO: A Hybrid Algorithm based on Artificial Bee Colony and teaching-learning-based optimization 37

ABC-TLBO: A Hybrid Algorithm Based on Artificial Bee Colony ...

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37th Chinese Control Conference (CCC)

作者： Zhang, Mei Pan, Yuchong Zhu, Jinhui Chen, Guangsen South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9789881563958

Artificial bee colony (ABC) is shown to be effective in solving optimization problems because of its good exploration capability. However it suffers from poor exploitation because onlooker bees use the same searching method as employed bees. In order to remedy the limited exploitation capability of ABC and achieve a better balance between exploration and exploitation, searching strategies for employed bees and onlooker bees are redesigned respectively and a novel algorithm called ABC-TLBO is proposed. It inherits the framework of basic ABC, and meanwhile an individualized TLBO based on teaching-learning-based optimization (TLBO) is embedded in onlooker bee phase to improve the exploitation capability of the presented algorithm. Moreover, a new searching strategy for employed bee phase which combines direction guiding searching and differential evolution crossover searching is applied to increase the population diversity and accelerate the convergence. The proposed ABC-TLBO algorithm is validated on benchmark functions and it is compared with ABC-based, TLBO-based and nature-inspired algorithms. Experimental results reveal that ABC-TLBO algorithm is an efficient, fast converging optimization method for solving global optimization problems.

关键词： Artificial bee colony teaching-learning-based optimization global numerical optimization

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CTLBO: Converged teaching-learning-based optimization

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COGENT ENGINEERING 2019年第1期6卷 Article: 1654207页

作者： Mahmoodabadi, M. J. Ostadzadeh, R. Sirjan Univ Technol Dept Mech Engn Sirjan Iran

teaching-learning-based optimization (TLBO) is an algorithm based on the influence of a teacher on the output of learners in a class. This method has shown to be more effective and efficient than other optimizations in finding the maximum solutions. In this paper, a new improved version of TLBO algorithm, called the converged teaching-learning-based optimization (CTLBO), is presented. In fact, it combines a proposed convergence operator with the teacher phase to find better solutions with a higher convergence rate. The method is tested on some benchmark problems and the results are compared with the original TLBO and other popular evolutionary algorithms. Furthermore, the introduced algorithm is used for optimization of fuzzy tracking control of a walking humanoid robot. In elaboration, fuzzy tracking control, which has appropriate membership functions and error indices, is employed in this paper as a promising intelligent approach to control the nonlinear dynamics of a humanoid robot. Summation of integrals of absolute angle errors and absolute control efforts is regarded as the objective function addressed by both TLBO and CTLBO algorithms in the present investigation.

关键词： teaching-learning-based optimization convergence operator benchmark problems humanoid robot fuzzy control

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Cuckoo search and teaching-learning-based optimization algorithms for optimum synthesis of path-generating four-bar mechanisms

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2017年第1期40卷 66-74页

作者： Lin, Wen-Yi Hsiao, Kuo-Mo De Lin Inst Technol Dept Mech Engn New Taipei Taiwan Natl Chiao Tung Univ Dept Mech Engn Hsinchu Taiwan

The optimization of one-phase dimensional synthesis problems for path-generating four-bar mechanisms is a difficult task. Such problems have mainly been solved in the literature using the differential evolution (DE) algorithm and its variants, such as a combined-mutation DE (CMDE). The shortcoming of using DE or its variants lies in the time-consuming parameter tuning that can take up to a dozen iterations. In this study, two well-known population-based metaheuristic optimization methods, i.e. the cuckoo search (CS) and teaching-learning-based optimization (TLBO) algorithms are employed to solve five representative problems. Only one user-supplied parameter is needed for CS and none for TLBO. Findings show that the solution accuracy of CS is superior to that of DE and TLBO. Moreover, CS, with its advantage of parameter tuning only four times, rivals CMDE in solution accuracy. The reason for the superior exploitation capability of CS lies in the special combination of two search modes, i.e. the Levy flight with respect to the best individual and the random walk. To bring the performance of TLBO into full play, the number of function evaluations is two times the original one. However, TLBO still fails to provide much more accurate solutions for the first two problems.

关键词： Optimum synthesis path generation cuckoo search teaching-learning-based optimization

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Neural network approach with teaching-learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

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NEURAL COMPUTING & APPLICATIONS 2017年第1-Sup期28卷 S737-S747页

作者： Kankal, Murat Uzlu, Ergun Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

This paper studies the performance of an artificial neural network (ANN) with teaching-learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey's EED. The results of the proposed model give excellent clues with regards to its use in future energy studies.

关键词： Electric energy demand teaching-learning-based optimization Artificial bee colony Backpropagation Neural network

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Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

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COMPUTERS & OPERATIONS RESEARCH 2017年第Jun.期82卷 102-113页

作者： Tang, Qiuhua Li, Zixiang Zhang, LiPing Zhang, Chaoyong Wuhan Univ Sci & Technol Dept Ind Engn Wuhan 430081 Hunan Peoples R China Huazhong Univ Sci Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hunan Peoples R China

Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic twosided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learhingbased optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Stochastic two-sided assembly line balancing teaching-learning-based optimization Variable neighborhood search Multiple constraints

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Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2017年第10期231卷 1871-1883页

作者： Lin, W. Y. Tsai, Y. H. Hsiao, K. M. De Lin Inst Technol Dept Mech Engn 1 Lane 380Qingyan Rd New Taipei 23654 Taiwan Natl Chiao Tung Univ Dept Mech Engn Hsinchu Taiwan

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

关键词： Geneva mechanism kinematic performance optimization teaching-learning-based optimization variable input speed

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Hierarchical multi-swarm cooperative teaching-learning-based optimization for global optimization

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SOFT COMPUTING 2017年第23期21卷 6983-7004页

作者： Zou, Feng Chen, Debao Lu, Renquan Wang, Peng HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Hierarchical cooperation mechanism, which is inspired by the features of specialization and cooperation in the social organizations, has been successfully used to increase the diversity of the population and avoid premature convergence for solving complex optimization problems. In this paper, a new two-level hierarchical multi-swarm cooperative TLBO variant called HMCTLBO is presented to solve global optimization problems. In the proposed HMCTLBO algorithm, all learners are randomly divided into several sub-swarms with equal amounts of learners at the bottom level of the hierarchy. The learners of each swarm evolve only in their corresponding swarm in parallel independently to maintain the diversity and improve the exploration capability of the population. Moreover, all the best learners from each swarm compose the new swarm at the top level of the hierarchy, and each learner of the swarm evolves according to Gaussian sampling learning. Furthermore, a randomized regrouping strategy is performed, and a subspace searching strategy based on Latin hypercube sampling is introduced to maintain the diversity of the population. To verify the performance of the proposed approaches, 48 benchmark test functions are evaluated. Conducted experiments indicate that the proposed HMCTLBO algorithm is competitive to some existing TLBO variants and other optimization algorithms.

关键词： Hierarchical multi-swarm cooperation teaching-learning-based optimization Gaussian sampling learning Regrouping Latin hypercube sampling

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