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Diversified teaching-learning-based optimization for fuzzy two-stage hybrid flow shop scheduling with setup time

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 4159-4173页

作者： Lei, Deming Xi, Bingjie Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Distributed scheduling has attracted much attention in recent years;however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem.

关键词： Two-stage hybrid flow shop scheduling distributed scheduling fuzzy scheduling teaching-learning-based optimization

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A modified teaching-learning-based optimization algorithm for numerical function optimization

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2019年第6期10卷 1357-1371页

作者： Niu, Peifeng Ma, Yunpeng Yan, Shanshan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Hydropower Stn Adm Taolinkou Reservoir Qinhuangdao 066004 Hebei Peoples R China

In this paper, a kind of modified teaching-learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.

关键词： teaching-learning-based optimization Modified teaching-learning-based optimization Exploratory and exploitative capabilities Unconstrained numerical functions CEC2017

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Augmented Lagrangian teaching-learning-based optimization for structural design

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2018年第12期232卷 2195-2213页

作者： Li, Hong-Shuang Dong, Qiao-Yue Yuan, Jiao-Yang Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Jiangsu Peoples R China

Stochastic optimization methods have been widely employed to find solutions to structural design optimization problems in the past two decades, especially for truss structures. The primary aim of this study is to introduce a design optimization method combining an augmented Lagrangian function and teaching-learning-based optimization for truss and nontruss structural design optimization. The augmented Lagrangian function serves as a constraint-handling tool in the proposed method and converts a constrained optimization problem into an unconstrained one. On the other hand, teaching-learning-based optimization is employed to resolve the transformed, unconstrained optimization problems. Since the proper values of the Lagrangian multipliers and penalty factors are unknown in advance, the proposed method is implemented in an iterative way to avoid the issue of selecting them, i.e. the Lagrangian multipliers and penalty factors are automatically updated according to the violation level of all constraints. To examine the performance of the proposed method, it is applied on a group of benchmark truss optimization problems and a group of nontruss optimization problems of aircraft wing structures. The computational results obtained by the proposed method are compared to the results produced by both other version of teaching-learning-based optimization and stochastic optimization methods.

关键词： Structural design optimization augmented Lagrangian method teaching-learning-based optimization constrained optimization truss structures nontruss structures

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Comparison of modified teaching-learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

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NEURAL COMPUTING & APPLICATIONS 2016年第7期27卷 2107-2122页

作者： Nayak, P. K. Mishra, S. Dash, P. K. Bisoi, Ranjeeta Synergy Inst Engn & Technol Dhenkanal India Centurion Univ Bhubaneswar Orissa India SOA Univ Bhubaneswar Orissa India

This paper presents a modified TLBO (teaching-learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.

关键词： LLRBFNN Power disturbance signals Feature extraction Cumulative sum average filter Pattern recognition Simultaneous power quality events teaching-learning-based optimization Extreme learning machine

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A modified teaching-learning-based optimization for optimal control of Volterra integral systems

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SOFT COMPUTING 2018年第17期22卷 5889-5899页

作者： Khanduzi, R. Ebrahimzadeh, A. Peyghami, M. Reza Gonbad Kavous Univ Dept Math & Stat POB 49717-99151 Gonbad Kavous Golestan Iran Islamic Azad Univ Najafabad Branch Young Researchers & Elite Club Najafabad Iran KN Toosi Univ Technol Fac Math POB 16315-1618 Tehran Iran

This study aimed to utilize a novel modified approach based on teaching-learning-based optimization (MTLBO), to achieve an approximate solution of optimal control problem governed by nonlinear Volterra integro-differential systems. The scheme was based upon Chebyshev wavelet and its derivative operational matrix, which eventually led to a nonlinear programming problem (NLP). The resulted NLP was solved by the MTLBO. The novel algorithm used a heuristic mechanism to intensify learning on the best students in learner phase. The new strategy was applied to improve learners' knowledge and to structure the MTLBO. The applicability and efficiency of the MTLBO were shown for three numerical examples. The proposed algorithm was compared with the traditional TLBO algorithm and the Legendre wavelets and collocation method in the literature. The experimental results showed that the proposed MTLBO not only obtained the high-quality solutions with respect to the absolute errors but also provided results with the high speed of convergence.

关键词： teaching-learning-based optimization Modified learner phase Optimal control Nonlinear Volterra integro-differential equation Collocation method Chebyshev wavelet

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Group-individual multi-mode cooperative teaching-learning-based optimization algorithm and an industrial engineering application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 5437-5465页

作者： Chen, Zhixiang Sun Yat Sen Univ Sch Business Dept Management Sci 135 West Xingang Rd Guangzhou 510275 Peoples R China

This paper modifies the original teaching-learning-based optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative teaching-learning-based optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem-warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues.

关键词： teaching-learning-based optimization group-individual multi-mode cooperation performance-based group teaching teacher self-learning team learning

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Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

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ENERGY CONVERSION AND MANAGEMENT 2019年第Apr.期186卷 293-305页

作者： Li, Shuijia Gong, Wenyin Yan, Xuesong Hu, Chengyu Bai, Danyu Wang, Ling Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Nanjing Forestry Univ Coll Econ & Management Nanjing 210037 Jiangsu Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Accurate and reliable parameter extraction of photovoltaic (PV) models is urgently desired for the simulation, evaluation, control, and optimization of PV systems. Although many meta-heuristic algorithms have been used to extract the PV parameters, the extracted parameters are usually not very accurate and reliable. To accurately and reliably extract the parameters of different PV models, an improved teaching-learning-based optimization (ITLBO) algorithm is proposed in this paper. The novelty of ITLBO lies primarily in the improved teaching and learning strategies with two improvements: (i) the teacher adopts different teaching strategies according to learner levels in the teacher phase;and (ii) in the learner phase, a new learning strategy is proposed to balance exploration and exploitation. The performance of ITLBO is verified by extracting the parameters of the single diode model, the double diode model, and three PV modules. The experimental results indicate that ITLBO obtains better performance with respect to accuracy and reliability compared to the other algorithms.

关键词： Photovoltaic models Parameter extraction teaching-learning-based optimization

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A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Jing Li, Debiao Tang, Hongtao Li, Xixing Lei, Deming Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan 430068 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.

关键词： Fabric dyeing process Fuzzy energy-efficient scheduling Batch processing machine teaching-learning-based optimization

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

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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

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