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Augmented Lagrangian teaching-learning-based optimization for structural design

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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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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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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Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2018年第1期39卷 65-77页

作者： Duan, Pei-yong Li, Jun-qing Wang, Yong Sang, Hong-yan Jia, Bao-xian Liaocheng Univ Coll Comp Sci Liaocheng Peoples R China Shandong Normal Univ Sch Informat Iinan Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

In this study, we present a novel teaching-learning-based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer-based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well-designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self-learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well-known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified.

关键词： energy conversation optimal chiller loading teaching-learning-based optimization

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Use of teaching learning based optimization for Data Clustering 1

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1st International Conference on Innovation in Electrical Power Engineering, Communication, and Computing Technology (IEPCCT)

作者： Naik, Anima KL Univ Hyderabad 500075 India

ISBN: (数字)9789811523052

ISBN: (纸本)9789811523052;9789811523045

In the literature, there are hundreds of population-based optimization algorithms, which are inspired by nature. These algorithms have been proposed to solve different problems of different research area. Among them, teaching-learning-based optimization (TLBO) algorithm is one, which is based on learner's behavior in the classroom. For improvement of performance of this algorithm, number of variants of TLBO algorithms already have been proposed. This paper compares the performance of these variants of TLBO in terms of clustering of unlabeled data sets. Here, it has been shown how different variants of TLBO can be used to find the clusters of a user-specified cluster numbers. These algorithms are evaluated by using some real-life datasets and compared their performances by some statistical tests.

关键词： teaching-learning-based optimization Data clustering Quantization error

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An improved teaching-learning-based optimization for design of frequency reconfigurable antennas

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第12期32卷 e4030-e4030页

作者： Zadehparizi, Fatemeh Jam, Shahrokh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran

This paper proposes an improved teaching-learning-based optimization (ITLBO) for the electromagnetics and antenna community. The basic TLBO was applied to many different problems with consistent success. For improving the algorithm performance, in the learner phase, each group of two learners is replaced by three-member groups. Furthermore, two new phases are added to the algorithm. The first phase involves selection of several better learners for teaching subjects to randomly selected weaker learners. The second phase is a mutation operation. The presented algorithm with these changes simulates a classroom more realistically and avoids being trapped in local optima. First, the ITLBO is evaluated to optimize seven benchmark functions and is shown to be faster and better. In addition, a frequency reconfigurable antenna design is considered as a practical problem and Taguchi's method is applied for adjusting the control parameters of the algorithm. Comparisons of the performance of this algorithm with those of the TLBO, two other improved versions of TLBO (I-TLBO and improved TLBO with learning experience of other learners [LETLBO]) and genetic algorithms demonstrate the higher efficiency for this algorithm in electromagnetic problems. A prototype of the proposed antenna is fabricated, and the simulation and measurement results agree well.

关键词： frequency reconfigurable antennas internal multiport method Taguchi's method teaching-learning-based optimization

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MTLBO: A Multi-objective Multi-course teaching-learning-based optimization Algorithm

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2018年第3期21卷 331-342页

作者： Lin, Wenwen Wang, Lei Tian, Guangdong Zhang, Yuejun Ningbo Univ Fac Mech Engn & Mech Ningbo 315211 Zhejiang Peoples R China Jilin Univ Transportat Coll Changchun 130022 Jilin Peoples R China

This paper presents a multi-objective evolutionary algorithm, which is called multi-course teaching-learning-based optimization algorithm. It consists of four phases: teacher, leaner, teacher training, and exchange student phases. Three novel modifications are introduced into the basic teaching-learning-based optimization algorithm. It helps diversify evolutionary population, enhance stochastic search ability, and avoid premature convergence. Eight widely used benchmark problems are employed in order to investigate the performance of the proposed algorithm. The experimental results indicate that it finds better Pareto optimal solutions than some other state-of-the-art algorithms for the majority of problems.

关键词： teaching-learning-based optimization Multi-objective optimization Evolutionary Algorithm Heuristic Algorithm

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Fuzzy adaptive teaching-learning-based optimization for global numerical optimization

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NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 309-327页

作者： Cheng, Min-Yuan Prayogo, Doddy Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sec 4Keelung Rd Taipei 106 Taiwan Petra Christian Univ Dept Civil Engn Jalan Siwalankerto 121-131 Surabaya 60236 Indonesia

teaching-learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching-learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching-learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization.

关键词： Metaheuristic Global optimization Fuzzy logic teaching-learning-based optimization Parameter tuning

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teaching-learning-based optimization algorithm for hybrid flow shop scheduling problem with assembly operations

Teaching-learning-based optimization algorithm for hybrid fl...

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第30届中国控制与决策会议

作者： Zhiwei Xu Deming Lei School of Automation Wuhan University of Technology

This paper addresses assembly-type hybrid flow shop scheduling problem(AHFSP) with the minimi-zation of total tardiness and makespan. The products have parts which are first processed in hybrid flow shop and then are assembled at the assembly stage. To solve this problem, a part-based solution representation is adopted and a teachers' teaching-learning-based optimization(TTLBO) is proposed. Teachers chosen from a non-dominated set undergo a self-learning phase and a teaching phase and these phases are implemented by multiple neighborhood search and global search, respectively. More-over, the learning phase of learners is removed and the learning of teachers is intensified to avoid the low-efficiency computation. Extensive experiments are conducted to test the performance of TTLBO. The computational results demonstrate that TTLBO is an effective and competitive method for multi-objective AHFSP.

关键词： Hybrid flow shop scheduling multi-objective optimization assembly teaching-learning-based optimization

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Three-objective optimization of boiler combustion process based on multi-objective teaching-learning based optimization algorithm and ameliorated extreme learning machine

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MACHINE learning WITH APPLICATIONS 2021年 5卷

作者： Ma, Yunpeng Wang, Heqi Zhang, Xinxin Hou, Likun Song, Jiancai Tianjin Univ Commerce Sch Informat Engn CO-300134 Tianjin Peoples R China

The combustion optimization problem of Circulation Fluidized Bed Boiler (CFBB) can be regarded as a constrained dynamic multi -objective optimization problem, so it has become a hot research to solve the problem for saving energy and reducing polluting gas. However, it is difficult to optimize the combustion process based on traditional optimization method due to a variety of complex characteristics of boiler, such as non -linearity, strong coupling , large lag. In order to address the boiler combustion optimization problem, a kind of multi -objective modified teaching-learning-based optimization (namely MMTLBO) is proposed. For the MMTLBO, a constrained mechanism is firstly introduced into MMTLBO. Finally, the MMTLBO and ameliorated extreme learning machine (AELM) are utilized to optimize the CFBB's combustion process for increasing the thermal efficiency and reducing the NOx/SO 2 emissions concentration. The AELM is used to establish the comprehensive model of the thermal efficiency and NOx/SO 2 emissions. The model accuracy and standard deviation can arrive 10 -2 and 10 -4 , separately. So the model shows high generalization ability and good stability. based on the model, the MMTLBO is applied to optimize the boiler's combustion process parameters. Experiment results show that the MMTLBO can find several groups reasonable combustion parameters which increase the thermal efficiency and reduce the NOx/SO 2 emissions concentration. Therefore, the AELM and MMTLBO are the effective artificial intelligence algorithms.

关键词： Multi-objective optimization Model teaching-learning-based optimization Extreme learning machine Boiler combustion optimization

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