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Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization

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RENEWABLE ENERGY 2020年 153卷 375-391页

作者： Zhou, Yuekuan Zheng, Siqian Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Hunan Univ Coll Civil Engn Minist Educ Key Lab Bldg Safety & Energy Efficiency Changsha 410082 Hunan Peoples R China

Integrating advanced materials in building glazing systems is critical for promoting net-zero energy buildings. In this research, both experimental and numerical studies were conducted on an aerogel glazing system. In order to provide climate adaptive designs on the aerogel glazing system with optimal geometric and operating parameters, a generic optimization methodology was developed by flexibly integrating supervised machine learning and advanced teaching-learning-based optimization algorithm. The proposed optimization methodology was thereafter used for optimal system designs in different climate regions. Results indicate that the proposed surrogate model can intelligently and accurately learn and update the optimization function with straightforward mathematical associations between multi-variables and objectives. In addition, within optimal cases, total heat gain and heat flux are dominated by the extinction coefficient in southern cities, whereas the total heat gain is dominated by the thermal conductivity in the northern city, LanZhou. By adopting the proposed technique in this study, compared to optimal results following the Taguchi standard orthogonal array, the total heat gain can be reduced by 62.5% to 36.27 kWh/m(2) in LanZhou, and by 5.9% to 267.18 kWh/m(2) in GuangZhou, respectively. This study formulates a general methodology for climate adaptive optimal designs on aerogel glazing systems in different climatic regions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Aerogel glazing system Climatic regions Machine learning optimization function teaching-learning-based optimization

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teaching-learning based optimization with Crossover Operation

Teaching-Learning Based Optimization with Crossover Operatio...

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第27届中国控制与决策会议

作者： Xiu-hong ZHAO Physics Department Anshan Normal University

This paper developed a new variant of teaching-learning-based optimization(TLBO),called teaching-learning-based optimization with Crossover(TLBOC),for improving the performance of *** TLBOC incorporated the conventional crossover operation of differential evolution(DE) algorithm into teaching phases,which aims at balancing local and global searching ***,an estimation of distribution operation is used to predict a learning *** learning elitist helps to boost learning efficiency of each student in learning *** performance of TLBOC is assessed for solving global unconstrained optimization functions with different *** to the TLBO and several other promising heuristic methods,numerical results reveal that the TLBOC has better optimization performance.

关键词： teaching-learning-based optimization Global searching capability Crossover Performance

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Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures

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KSCE JOURNAL OF CIVIL ENGINEERING 2014年第6期18卷 1759-1767页

作者： Dede, Tayfun Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

The aim of this study is to present a new efficient optimization algorithm called teaching-learning-based optimization (TLBO). The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several benchmark problem related truss structures with discrete design variables are used to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

关键词： teaching-learning-based optimization discrete optimization truss structure weight minimization

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Power system economic emission dispatch considering uncertainties of wind, solar, and small runoff hydropower via a hybrid multi-objective optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Xiong, Guojiang Liu, Qinghua Wang, Yang Fu, Xiaofan Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China State Grid Chongqing Elect Power Co Wanzhou Power Supply Branch Chongqing 404100 Peoples R China

Economic emission dispatch (EED) plays a key role for the power system operation. With the integration of renewable energy sources (RESs), their uncertainties pose great challenges to the EED. This work establishes an EED model of thermal generators, wind, solar, and small runoff hydropower and considers varied practical factors and constraints. Overestimation and underestimation models are used to describe the uncertainties. Meanwhile, to solve this model effectively, a hybrid multi-objective method MOAGT by combining Archimedes optimization algorithm, artificial gorilla troops optimizer, and teaching-learning-based optimization is presented via both parallelization and serialization. Some techniques including chaotic opposition initialization, Morlet wavelet mutation, memory retention, and modified compromise solution selection are utilized to raise the performance. The validity of the proposed model with MOAGT is demonstrated on two systems. Results compared with peer algorithms and the CPLEX solver show that MOAGT achieves better solutions in less costs and lower emissions, which can make the systems run in a more economical and low-carbon state under the premise of meeting the system reliability. Besides, the established EED model has good portability that supports the transition to a cleaner energy system. Furthermore, MOAGT can be seen as an effective alternate to the EED problem with RESs.

关键词： Artificial gorilla troops optimizer Archimedes optimization algorithm Economic emission dispatch Renewable energy teaching-learning-based optimization Uncertainty

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Modified Marine Predators Algorithm hybridized with teaching- learning mechanism for solving optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 93-127页

作者： Ma, Yunpeng Chang, Chang Lin, Zehua Zhang, Xinxin Song, Jiancai Chen, Lei Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Commerce Coll Sci Tianjin 300134 Peoples R China

Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Levy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.

关键词： meta-heuristics optimization Marine Predators Algorithm exploitation and exploration modified Marine Predators Algorithm teaching-learning-based optimization

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Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第6期20卷 10090-10134页

作者： Wu, Di Wen, Changsheng Rao, Honghua Jia, Heming Liu, Qingxin Abualigah, Laith Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan

The reptile search algorithm (RSA) is a bionic algorithm proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, the encircling stage includes high walking and belly walking, and the hunting stage includes hunting coordination and cooperation. However, in the middle and later stages of the iteration, most search agents will move towards the optimal solution. However, if the optimal solution falls into local optimum, the population will fall into stagnation. Therefore, RSA cannot converge when solving complex problems. To enable RSA to solve more problems, this paper proposes a multi-hunting coordination strategy by combining Lagrange interpolation and teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation strategy will make multiple search agents coordinate with each other. Compared with the hunting cooperation strategy in the original RSA, the multi-hunting cooperation strategy has been greatly improved RSA's global capability. Moreover, considering RSA's weak ability to jump out of the local optimum in the middle and later stages, this paper adds the Lens pposition-based learning (LOBL) and restart strategy. based on the above strategy, a modified reptile search algorithm with a multi-hunting coordination strategy (MRSA) is proposed. To verify the above strategies' effectiveness for RSA, 23 benchmark and CEC2020 functions were used to test MRSA's performance. In addition, MRSA's solutions to six engineering problems reflected MRSA's engineering applicability. It can be seen from the experiment that MRSA has better performance in solving test functions and engineering problems.

关键词： reptile search algorithm Lagrange interpolation teaching-learning-based optimization benchmark function test lens opposition-based learning restart strategy

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A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization

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APPLIED INTELLIGENCE 2023年第10期53卷 12448-12471页

作者： Liu, Nengxian Pan, Jeng-Shyang Chu, Shu-Chuan Lai, Taotao Fuzhou Univ Coll Comp & Data Sci 2 Wulongjiang North Ave Fuzhou 350108 Fujian Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China Chaoyang Univ Technol Dept Informat Management 168 Jifeng E Rd Taichung 41349 Taiwan Minjiang Univ Coll Comp & Control Engn 200 Xiyuangong Rd Fuzhou 350108 Fujian Peoples R China

This article introduces an efficient surrogate-assisted bi-swarm evolutionary algorithm (SABEA) with hybrid and ensemble strategies for computationally expensive optimization problems. In SABEA, the evolutionary swarm is randomly partitioned into two sub-swarms to maintain diversity of the population. One sub-swarm evolves using the differential evolution (DE) and the other one evolves using teaching-learning-based optimization (TLBO). The proposed SABEA has strong exploration and exploitation capabilities by taking advantages of these two powerful algorithms. Besides, both the global and the local surrogate models cooperate effectively in the proposed SABEA for estimating the fitness value. The global model is established with all samples in the database for global search, and the local model is created with training samples around the current swarm for local search. In addition, a restart mechanism and two model management schemes, namely the individual-based and generation-based, are effectively integrated in the proposed algorithm to make SABEA more strong. Twenty benchmark functions and the tension/compression spring design problem are employed to assess the proposed SABEA. Comprehensive experimental results demonstrate that our proposed SABEA has superior performance comparing with several state-of-the-art competing algorithms on most of the test problems with low, medium and high dimensions.

关键词： Surrogate-assisted Expensive problems Bi-swarm evolutionary algorithm Differential evolution teaching-learning-based optimization

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Structural optimization with teaching-learning-based optimization algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第4期47卷 495-511页

作者： Dede, Tayfun Ayvaz, Yusuf Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey Yildiz Tech Univ Dept Civil Engn TR-34220 Istanbul Turkey

In this paper, a new efficient optimization algorithm called teaching-learning-based optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

关键词： teaching-learning-based optimization truss structure weight minimization

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Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2013年 126卷 11-20页

作者： Li, Guoqiang Niu, Peifeng Zhang, Weiping Liu, Yongchao Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China

The teaching-learning-based optimization (TLBO) is a new efficient optimization algorithm. To improve the solution quality and to quicken the convergence speed and running time of TLBO, this paper proposes an ameliorated TLBO called A-TLBO and test it by classical numerical function optimizations. Compared with other several optimization methods, A-TLBO shows better search performance. In addition, the A-TLBO is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build NOx emissions model of a 330MW coal-fired boiler and obtain a well-generalized model. Experimental results show that the tuned LS-SVM model by A-TLBO has well regression precision and generalization ability. (C) 2013 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Least squares support vector machine NOx emissions Coal-fired boiler

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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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