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Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第1期26卷 430-445页

作者： Rao, R. Venkata Patel, Vivek SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat 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 a two stage thermoelectric cooler (TEC). Two different arrangements of the thermoelectric cooler are considered for the optimization. Maximization of cooling capacity and coefficient of performance of the thermoelectric cooler are considered as the objective functions. An example is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization obtained by using the modified TLBO are validated by comparing with those obtained by using the basic TLBO, genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Two stage thermoelectric cooler Cooling capacity Coefficient of performance Thermoelectric modules teaching-learning-based optimization

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Sizing truss structures using teaching-learning-based optimization

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COMPUTERS & STRUCTURES 2013年 119卷 177-188页

作者： Degertekin, S. O. Hayalioglu, M. S. Dicle Univ Dept Civil Engn TR-21280 Diyarbakir Turkey

Meta-heuristic search methods have been extensively used for optimization of truss structures over the past two decades. In this study, a new meta-heuristic search method called 'teaching-learning-based optimization' (TLBO) is applied for optimization of truss structures. The method makes use of the analogy between the learning process of learners and searching for designs to optimization problems. The TLBO consists of two phases: teacher phase and learner phase. 'Teacher phase' means learning from the teacher and 'learner phase' means learning by the interaction between learners. The validity of the method is demonstrated by the four design examples. Results obtained for the design examples revealed that although the TLBO developed slightly heavier designs than the other meta-heuristic methods in a few cases, it obtained results as good as or better than the other meta-heuristic optimization methods in terms of both the optimum solutions and the convergence capability in most cases. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Structural optimization Truss structures teaching-learning-based optimization

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Hierarchical multi-swarm cooperative teaching-learning-based optimization for global optimization

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SOFT COMPUTING 2017年第23期21卷 6983-7004页

作者： Zou, Feng Chen, Debao Lu, Renquan Wang, Peng HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Hierarchical cooperation mechanism, which is inspired by the features of specialization and cooperation in the social organizations, has been successfully used to increase the diversity of the population and avoid premature convergence for solving complex optimization problems. In this paper, a new two-level hierarchical multi-swarm cooperative TLBO variant called HMCTLBO is presented to solve global optimization problems. In the proposed HMCTLBO algorithm, all learners are randomly divided into several sub-swarms with equal amounts of learners at the bottom level of the hierarchy. The learners of each swarm evolve only in their corresponding swarm in parallel independently to maintain the diversity and improve the exploration capability of the population. Moreover, all the best learners from each swarm compose the new swarm at the top level of the hierarchy, and each learner of the swarm evolves according to Gaussian sampling learning. Furthermore, a randomized regrouping strategy is performed, and a subspace searching strategy based on Latin hypercube sampling is introduced to maintain the diversity of the population. To verify the performance of the proposed approaches, 48 benchmark test functions are evaluated. Conducted experiments indicate that the proposed HMCTLBO algorithm is competitive to some existing TLBO variants and other optimization algorithms.

关键词： Hierarchical multi-swarm cooperation teaching-learning-based optimization Gaussian sampling learning Regrouping Latin hypercube sampling

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Dynamic opposite learning enhanced teaching-learning-based optimization

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KNOWLEDGE-based SYSTEMS 2020年 188卷 104966-104966页

作者： Xu, Yunlang Yang, Zhile Li, Xiaoping Kang, Huazhou Yang, Xiaofeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Fudan Univ Sch Microelect State Key Lab ASIC & Syst Shanghai 200433 Peoples R China

The teaching-learning-based optimization (TLBO) algorithm has been one of most popular bio-inspired meta-heuristic algorithms due to the competitive converging speed and high accuracy. A batch of TLBO variants has been proposed to enhance the exploitation ability and accelerate the exploration process. However, they still suffer from premature convergence in solving complex non-linear problems. In the study, a novel TLBO variant named dynamic-opposite learning TLBO (DOLTLBO) is proposed, which employs a new dynamic-opposite learning (DOL) strategy to overcome premature convergence. The search space of DOL has the characteristics of asymmetry and dynamically adjusting along with a random opposite number. The asymmetric search space significantly increase the probability for the population in obtaining the global optimum, which holistically improves the exploitation capability of DOLTLBO. Meanwhile, the dynamically changing characteristic enriches the diversity of the search space, thus enhancing the exploration ability. To validate the proposed DOL operator and DOLTLBO algorithm, shifted and rotated benchmark functions from CEC 2014, multiextremal functions and constrained engineering problems have been experimented upon. Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： teaching-learning-based optimization Dynamic-opposite learning Opposition-based learning Global optimization

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An Elite-Class teaching-learning-based optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

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Computers, Materials & Continua 2024年第4期79卷 47-63页

作者： Deming Lei Surui Duan Mingbo Li Jing Wang College of Automation Wuhan University of TechnologyWuhan430070China

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling *** this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion *** produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are *** teacher phase is composed of teacher competition and teacher *** learner phase is replaced with a reinforcement search of the elite *** adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in *** experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

关键词： Hybrid flow shop scheduling reentrant bottleneck stage teaching-learning-based optimization

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Prediction of berm geometry using a set of laboratory tests combined with teaching-learning-based optimization and artificial bee colony algorithms

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APPLIED OCEAN RESEARCH 2014年 48卷 103-113页

作者： Uzlu, Ergun Komurcu, Murat Ihsan Kankal, Murat Dede, Tayfun Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey

Understanding sediment movement in coastal areas is crucial in planning the stability of coastal structures, the recovery of coastal areas, and the formation of new coast. Accretion or erosion profiles form as a result of sediment movement. The characteristics of these profiles depend on the bed slope, wave conditions, and sediment properties. Here, experimental studies were performed in a wave flume with regular waves, considering different values for the wave height (H-0), wave period (T), bed slope (m), and mean sediment diameter (d(50)). Accretion profiles developed in these experiments, and the geometric parameters of the resulting berms were determined. teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were applied to regression functions of the data from the physical model. Dimensional and dimensionless equations were found for each parameter. These equations were compared to data from the physical model, to determine the best equation for each parameter and to evaluate the performances of the TLBO and ABC algorithms in the estimation of the berm parameters. Compared to the ABC algorithm, the TLBO algorithm provided better accuracy in estimating the berm parameters. Overall, the equations successfully determined the berm parameters. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Coastal profiles Cross-shore sediment transport Berm Accretion teaching-learning-based optimization Artificial bee colony algorithm

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Simulation of global MPPT based on teaching-learning-based optimization technique for partially shaded PV system

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ELECTRICAL ENGINEERING 2017年第3期99卷 847-859页

作者： Rezk, Hegazy Fathy, Ahmed Menia Univ Fac Engn Elect Engn Dept Al Minya Egypt Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

The power against voltage curve for PV power system during shadow condition contains number of local maximum power points (MPPs) and only one global. The classical maximum power point tracking (MPPT) algorithms are designed to follow the global MPP, but they stuck around local MPPs such as fuzzy logic controller (FLC). Therefore, A global MPPT based on teaching-learning-based optimization (TLBO) algorithm has been presented in this paper. The performance of PV system under abnormal conditions such as partial shading has been improved. TLBO algorithm is simple computational steps and faster convergence to optimal solution. A comprehensive assessment of TLBO-based tracker is carried out against FLC and particle swarm optimization (PSO) techniques for same conditions. Six different partial shading patterns have been employed to investigate TLBO performance using MATLAB/Simulink. The parameters of comparison include the tracking speed and overall tracking efficiency. The results confirm that TLBO-based tracker exactly convergence to global MPP under different studied cases. TLBO has best performance compared to the other studied techniques. The tracking speed is increased using TLBO-based tracker;the average tracking time of global MPP is reduced by more than 23.8 % compared with PSO in all studied different partial shading patterns.

关键词： teaching-learning-based optimization Particle swarm optimization Global MPPT Partial shading Fuzzy logic control

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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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Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching-learning-based optimization methods

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 1103-1120页

作者： Hassanzadeh, Yousef Jafari-Bavil-Olyaei, Amin Aalami, Mohammad-Taghi Kardan, Nazila Univ Tabriz Dept Civil Engn Tabriz Iran Azarbaijan Shahid Madani Univ Dept Civil Engn Tabriz Iran

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching-learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.

关键词： Adaptive neuro-fuzzy inference system teaching-learning-based optimization Empirical equations Scour depth Bridge pier

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Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching-learning-based optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第6期234卷 1146-1155页

作者： Venu, Borigorla Raju, L. Suvarna Rao, K. Venkata Vignans Fdn Sci Technol & Res Univ Dept Mech Engn Guntur 522213 Andhra Pradesh India

This study focuses on optimization of process parameters, which may result in improved mechanical properties of the friction stir weldments of AA2014-T651. Plain taper and threaded taper cylindrical tool pin profiles were used for the study. A set of experiments was conducted at different levels of tool rotational and weld speeds using two tool pin profiles. Mechanical properties such as tensile strength, yield strength, impact strength, percentage of elongation, and hardness were measured. Objective functions are developed for the five mechanical properties in terms of input parameters. The input parameters were optimized using teaching-learning-based optimization algorithm technique to improve mechanical properties. The teaching-learning-based optimization algorithm suggested three best combinations such as combination-I (940 r/min and 32 mm/min), combination-II (1100 r/min and 40 mm/min), and combination-III (1205 r/min and 45 mm/min). The optimization is also validated with experimental results.

关键词： Friction stir welding rotational speed weld speed teaching-learning-based optimization mechanical properties mathematical models multiobjective optimization

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