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

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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

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Hybrid teaching-learning-based optimization for solving engineering and mathematical problems

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第9期44卷 1-31页

作者： Dastan, Mohammadhossein Shojaee, Saeed Hamzehei-Javaran, Saleh Goodarzimehr, Vahid Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In this work, a new and effective algorithm called hybrid teaching-learning-based optimization (TLBO) and charged system search (CSS) algorithms (HTC) are proposed to solve engineering and mathematical problems. The CSS is inspired by Coulomb and Gauss's electrostatic laws of physics as well as the Newtonian mechanic laws of motion. The TLBO is inspired by the interaction between teacher and student in a classroom. Usually, the TLBO gets trapped in the local optimal due to the lack of a system for measuring the distance between the student and the optimal point. In order to solve this problem, the CSS algorithm, which is based on the electrical physics laws, is utilized. In the CSS algorithm, each factor is stored under the influence of the best local and global positions, and it is used in subsequent iterations as the possible optimal answers. In fact, this leads to a better balance between exploration and exploitation. In order to validate the proposed method, the CEC2021 and CEC2005 mathematical functions are optimized. Additionally, to show the applicability of the proposed algorithm and to evaluate its performance and convergence rate, several benchmark truss structures are optimized. The weight of the structural elements is taken into account as the objective function, which is optimized under displacement and stress constraints. The results of the proposed algorithm are compared with some other well-known meta-heuristic methods. The results show that the hybrid HTC algorithm improved the convergence rate and quickly obtained the optimal and desired design. The hybrid HTC algorithm can be adapted to solve other complex mathematical and optimization problems.

关键词： Meta-heuristic algorithms optimization of structures Charged system search algorithm teaching-learning-based optimization Hybrid charged system search based on TLBO algorithm

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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 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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An ameliorated teaching-learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur's entropy and Otsu's between class variance

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INFORMATION SCIENCES 2020年 533卷 72-107页

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

In this paper, multi-threshold image segmentation approaches using an improved teaching-learning-based optimization algorithm (DI-TLBO) are presented and the proposed DI-TLBO-based methods obtain satisfactory segmentation results. This work is presented as follows. Firstly, two random numbers are introduced to determine the learning methods of the learner in the teacher phases and the learner phases of DI-TLBO. Randomness of the learning methods further improves global optimization ability of DI-TLBO. Self-feedback learning phase and mutation-crossover phase are also introduced into DI-TLBO algorithm, which makes DI-TLBO achieve better exploration ability. The comparative results of DI-TLBO with other evolutionary algorithms (EAs) on a set of benchmarks functions demonstrate that DI-TLBO acquires better solution accuracy than other EAs. Then the proposed DI-TLBO algorithm is applied to solve multi-level threshold image segmentation problems modeled by Otsu's between class variance function and Kapur's entropy function. Experiments comparing DI-TLBO-based methods with other EAs based approaches on standard test images show that DI-TLBO-based methods possess superior performance in terms of both solution accuracy and stability of segmentation results. Finally, the proposed DI-TLBO-based methods are successfully applied in casting X-ray image segmentation for multi-level threshold. Although the defects in high resolution X-ray image (3072 x 2400) are easy to be ignored and omitted when being detected artificially, all the defects are segmented perfectly using the proposed DI-TLBO-based methods. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Multilevel thresholding Image segmentation teaching-learning-based optimization Otsu's threshold Kapur's entropy

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teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

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ENGINEERING optimization 2012年第12期44卷 1447-1462页

作者： Rao, R. V. Savsani, V. J. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat Gujarat India Univ Maribor Fac Mech Engn Maribor Slovenia

An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, epsilon-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.

关键词： real-parameter optimization unconstrained benchmark functions constrained benchmark functions teaching-learning-based optimization

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Quadratic interpolation based teaching-learning-based optimization for chemical dynamic system optimization

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KNOWLEDGE-based SYSTEMS 2018年 145卷 250-263页

作者： Chen, Xu Mei, Congli Xu, Bin Yu, Kunjie Huang, Xiuhui Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Shanghai Univ Engn Sci Sch Mech Engn Shanghai 201620 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai 200093 Peoples R China

Optimal design and control of industrially important chemical processes rely on dynamic optimization. However, because of the highly constrained, nonlinear, and sometimes discontinuous nature that is inherent in chemical processes, solving dynamic optimization problems (DOPs) is still a challenging task. teaching-learning-based optimization (TLBO) is a relative new metaheuristic algorithm based on the philosophy of teaching and learning. In this paper, we propose an improved TLBO called quadratic interpolation based TLBO (QITLBO) for handling DOP5 efficiently. In the QITLBO, two modifications, namely diversity enhanced teaching strategy and quadratic interpolation operator, are introduced into the basic TLBO. The diversity enhanced teaching strategy is employed to improve the exploration ability, and the quadratic interpolation operator is used to enhance the exploitation ability;therefore, the ensemble of these two components can establish a better balance between exploration and exploitation. To test the performance of the proposed method, QITLBO is applied to solve six chemical DOPs include three parameter estimation problems and three optimal control problems, and compared with eleven well-established metaheuristic algorithms. Computational results reveal that QITLBO has the best precision and reliability among the compared algorithms for most of the test problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Dynamic system optimization Chemical processes Global optimization teaching-learning-based optimization Quadratic interpolation

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Constrained optimization based on improved teaching-learning-based optimization algorithm

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INFORMATION SCIENCES 2016年 352卷 61-78页

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

This paper proposes an improved constrained teaching-learning-based optimization (ICTLBO) method to efficiently solve constrained optimization problems (COPs). In the teacher phase of ICTLBO, the population is partitioned into several subpopulations, and the direction information between the mean position of each subpopulation and the best position of population guide the corresponding subpopulation to the promising region promptly. Information exchange between different subpopulations is used to discourage premature convergence of each subpopulation. Furthermore, in the learner phase, a new learning strategy is introduced to improve the population diversity and enhance the global search ability. Three different constraint handling methods are adopted for three situations, which are infeasible, semi-feasible, and feasible situations, during the evolution process. To evaluate the performance of ICTLBO, 22 benchmark functions presented in CEC2006 and 18 benchmark functions introduced in CEC2010 are chosen as the test suite. Moreover, four widely used engineering design problems are selected to test the performance of ICTLBO for real-world problems. Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms. (C) 2016 Elsevier Inc. All rights ***

关键词： teaching-learning-based optimization Constrained optimization Constraint handling learning strategy

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Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

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COMPUTERS & CHEMICAL ENGINEERING 2017年 99卷 314-324页

作者： Yu, Kunjie While, Lyndon Reynolds, Mark Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Univ Western Australia Sch Comp Sci & Software Engn Nedlands WA 6009 Australia Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ethylene cracking furnace Cyclic scheduling teaching-learning-based optimization

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Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm

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APPLIED MATHEMATICAL MODELLING 2013年第3期37卷 1147-1162页

作者： Rao, R. Venkata Patel, Vivek SV Natl Inst Technol Dept Mech Engn Surat 395007 India

teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of heat exchangers. Plate-fin heat exchanger and shell and tube heat exchanger are considered for the optimization. Maximization of heat exchanger effectiveness and minimization of total cost of the exchanger are considered as the objective functions. Two examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using the modified TLBO are validated by comparing with those obtained by using the genetic algorithm (GA). (C) 2012 Elsevier Inc. All rights reserved.

关键词： Plate-fin heat exchanger Shell and tube heat exchanger teaching-learning-based optimization Genetic algorithm

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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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