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Two-stage teaching-learning-based optimization method for flexible job-shop scheduling under machine breakdown

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第5-8期100卷 1419-1432页

作者： Buddala, Raviteja Mahapatra, Siba Sankar Natl Inst Technol Rourkela Dept Mech Engn Rourkela India

In the real-world situations, uncertain events commonly occur and cause disruption of normal scheduled activities. Consideration of uncertain events during the scheduling process helps the organizations to make strategies for handling the uncertainties in an effective manner. Therefore, in the present paper, unexpected machine breakdowns have been considered during scheduling of jobs in a flexible job-shop environment. The objective is to obtain lowest possible makespan such that robust and stable schedules are produced even if an unexpected machine breakdown occurs. The robust and stable schedules may help to decrease the costs associated with unexpected machine failures. The present work uses a two-stage teaching-learning-based optimization (2S-TLBO) method to solve flexible job-shop scheduling problem (FJSP) under machine breakdown. In the first stage, the primary objective of makespan is optimized without considering any machine breakdown. In the second stage, a bi-objective function considering robustness and stability of the schedule is optimized under uncertainty of machine breakdowns. In order to incorporate the machine breakdown data to basic FJSP, a non-idle time insertion technique is used. In order to generate effective robust and stable predictive FJSP schedules, a rescheduling technique called modified affected operations rescheduling (mAOR) is used. The Kacem's and Brandimarte's benchmark problems have been solved and compared with other algorithms available in the literature. Results indicate that TLBO outperforms other algorithms by generating superior robust and stable predictive schedules. Statistical analysis is carried out to test the significance difference of the results obtained by TLBO with other algorithms.

关键词： Flexible job-shop scheduling Machine breakdown Makespan Meta-heuristics Robustness Stability teaching-learning-based optimization

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A Hybrid Two-Stage teaching-learning-based optimization Algorithm for Feature Selection in Bioinformatics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1746-1760页

作者： Kang, Yan Wang, Haining Pu, Bin Tao, Liu Chen, Jianguo Yu, Philip S. Yunnan Univ Natl Pilot Sch Software Kunming 650106 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou 510275 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing 100084 Peoples R China

The "curse of dimensionality" brings new challenges to the feature selection (FS) problem, especially in bioinformatics filed. In this paper, we propose a hybrid Two-Stage teaching-learning-based optimization (TS-TLBO) algorithm to improve the performance of bioinformatics data classification. In the selection reduction stage, potentially informative features, as well as noisy features, are selected to effectively reduce the search space. In the following comparative self-learning stage, the teacher and the worst student with self-learning evolve together based on the duality of the FS problems to enhance the exploitation capabilities. In addition, an opposition-based learning strategy is utilized to generate initial solutions to rapidly improve the quality of the solutions. We further develop a self-adaptive mutation mechanism to improve the search performance by dynamically adjusting the mutation rate according to the teacher's convergence ability. Moreover, we integrate a differential evolutionary method with TLBO to boost the exploration ability of our algorithm. We conduct comparative experiments on 31 public data sets with different data dimensions, including 7 bioinformatics datasets, and evaluate our TS-TLBO algorithm compared with 11 related methods. The experimental results show that the TS-TLBO algorithm obtains a good feature subset with better classification performance, and indicates its generality to the FS problems.

关键词： Feature selection teaching-learning-based optimization data classification hybrid search

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Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison teaching-learning-based optimization

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IEEE ACCESS 2021年 9卷 52425-52441页

作者： Li, Lei Xiong, Guojiang Yuan, Xufeng Zhang, Jing Chen, Jinlong Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Guizhou Elect Power Grid Dispatching & Control Ct Guiyang 550002 Peoples R China

Parameter extraction of solar cell models plays an important role in the simulation, evaluation, control, and optimization of the photovoltaic (PV) system. Although many meta-heuristic algorithms have been proposed to solve the parameter extraction, it is necessary to further improve the accuracy and reliability of these algorithms. In this paper, an optimized teaching-learning-based optimization (TLBO) is proposed, namely dynamic self-adaptive and mutual-comparison teaching-learning-based optimization (DMTLBO). DMTLBO enhances the basic TLBO by improving its teacher phase and learner phase: (i) In the teacher phase, two differentiated and personalized teaching strategies are proposed according to learners' learning status. In these two strategies, an adaptive state transition weight factor omega and a dynamic gap weight factor beta are introduced to reflect the dynamic transformation of the learners' learning state in the actual teaching situation. (ii) In the learner phase, a new learning strategy is proposed. The learner can communicate and learn with three different learners who are randomly selected and ranked. To verify the performance of the DMTLBO algorithm, it is used to extract the parameters of different PV models, such as the single diode model, the double diode model, and three PV modules. Among these PV models, the root mean square error values between the measured data and the calculated data of DMTLBO are 9.8602E-04 +/- 2.07E-17, 9.8248E-04 +/- 1.53E-06, 2.4251E-03 +/- 2.15E-17, 1.7298E-03 +/- 5.74E-14, and 1.6601E-02 +/- 4.55E-10, respectively. Compared with other optimization algorithms, the experimental results show that DMTLBO can provide better or highly competitive convergence speed and extraction accuracy. Besides, the influence of the improved teacher phase and learner phase on DMTLBO and the changing process of both weight factors omega and beta are investigated.

关键词： optimization Integrated circuit modeling Heuristic algorithms Semiconductor diodes Parameter extraction Equivalent circuits Education Photovoltaic parameter extraction teaching-learning-based optimization

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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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Sampling CAD models via an extended teaching-learning-based optimization technique

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COMPUTER-AIDED DESIGN 2018年 100卷 52-67页

作者： Khan, Shahroz Gunpinar, Erkan Istanbul Tech Univ Istanbul Turkey

The teaching-learning-based optimization (TLBO) algorithm of Rao et al. has been presented in recent years, which is a population-based algorithm and operates on the principle of teaching and learning. This algorithm is based on the influence of a teacher on the quality of learners in a population. In this study, TLBO is extended for constrained and unconstrained CAD model sampling which is called Sampling-TLBO (S-TLBO). Sampling CAD models in the design space can be useful for both designers and customers during the design stage. A good sampling technique should generate CAD models uniformly distributed in the entire design space so that designers or customers can well understand possible design options. To sample N designs in a predefined design space, N sub-populations are first generated each of which consists of separate learners. teaching and learning phases are applied for each sub-population one by one which are based on a cost (fitness) function. Iterations are performed until change in the cost values becomes negligibly small. Teachers of each sub-population are regarded as sampled designs after the application of S-TLBO. For unconstrained design sampling, the cost function favors the generation of space-filling and Latin Hypercube designs. Space-filling is achieved using the Audze and Eglais' technique. For constrained design sampling, a static constraint handling mechanism is utilized to penalize designs that do not satisfy the predefined design constraints. Four CAD models, a yacht hull, a wheel rim and two different wine glasses, are employed to validate the performance of the S-TLBO approach. Sampling is first done for unconstrained design spaces, whereby the models obtained are shown to users in order to learn their preferences which are represented in the form of geometric constraints. Samples in constrained design spaces are then generated. According to the experiments in this study, S-TLBO outperforms state-of-the-art techniques particularly when

关键词： teaching-learning-based optimization Design sampling Computer-aided design Generative design

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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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Improved teaching-learning-based optimization algorithm based on fusion difference mutation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4643-4651页

作者： Liang, Shaohui Wei, Botao Xian Univ Sci & Technol Sch Sci Dept Math Xian 710054 Peoples R China

teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm.

关键词： teaching-learning-based optimization adaptive teaching factors the improved teaching stage learning stages differential mutation

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Minimizing cycle time and energy consumption for a multi-degree serial manipulator using teaching-learning-based optimization

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第5期45卷 1-12页

作者： Shinde, V. B. Pawar, P. J. Savitribai Phule Pune Univ KK Wagh Inst Engn Educ & Res Dept Prod Engn Pune 422003 Maharashtra India Savitribai Phule Pune Univ Amrutvahini Coll Engn Dept Prod Engn Pune 422608 Maharashtra India

Multiple inverse kinematic solutions are obtained for a multi-degree serial manipulator. Each solution provides a different cycle time and energy consumed to perform a given task in a particular sequence. Improper selection of inverse kinematic solution in a robotic assembly line may lead to an increase in not only overall cycle time but also energy consumption. Therefore, it is necessary to optimize the robot's performance with respect to cycle time and energy consumption simultaneously. These two objectives usually conflict due to joint motors' different operational speeds and energy consumption rates. Hence, in this work, an attempt is made to simultaneously optimize both cycle time and energy consumption by minimizing the differences in joint angles of the robot manipulator while moving from one position to another in applications involving multi-point movement of the robot end effector. The proposed approach is demonstrated by considering an example of a commonly used six-degree-of-freedom Kuka KR5-A robot manipulator. It can be extended efficiently for any other robot manipulator. A relatively new algorithm known as teaching-learning-based optimization (TLBO) algorithm is employed to solve this problem. It is observed that the results obtained by TLBO show a significant reduction of about 20% and 10% for cycle time, while 3% and 15% for energy consumption over the results obtained by genetic algorithm (GA) and sequential quadratic programming (SQP), respectively.

关键词： teaching-learning-based optimization Cycle time Energy consumption Multi-degree serial manipulator Inverse robot kinematics

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Multi-objective optimization using teaching-learning-based optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第4期26卷 1291-1300页

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

Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPS). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： teaching-learning-based optimization Multi-objective optimization Nondominated sorting Crowding distance

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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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