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Optimal design of the heat pipe using TLBO (teaching learning-based optimization) algorithm

ENERGY 2015年 80卷 535-544页

作者： Rao, R. V. More, K. C. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (teaching-learning-based optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heat pipe Multi-objective optimization Micro-grooves teaching-learning-based optimization algorithm

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Optimal PMU Configuration based on Improving teaching-learning-based optimization algorithm

Optimal PMU Configuration Based on Improving Teaching-learni...

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2018 3rd International Conference on Electrical, Automation and Mechanical Engineering(EAME 2018)

作者： Xiaodong Li Mingyu Jing School of Electrical Engineering & Information Engineering Lanzhou University of Technology State Grid Gansu Electric Power Company Qingyang Power Supply Company

Taking the full network observability of power system operation state and the least number of phasor measurement units(PMU) as objective, this paper proposes an optimal PMU placement algorithm based on an improving teaching-learning-based optimization algorithm(ITLBO). Phasor measurement units can measure bus voltage phasors, combined with rapid topological observable analysis method, the power system can be fully observable. And then proposes the improving teaching-learning optimization algorithm to solve the optimal configuration problems, to achieve the global optimum, finally get optimal configuration scheme of measuring points. Finally harmonic state estimation is carried out on the basis of this, and programme in matlab and compare with the binary teachinglearning optimization algorithm verify its validity of the proposed method.

关键词： teaching-learning-based optimization algorithm phasor measurement units Optimal PMU configuration harmonic state estimation

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Novel teaching-learning-based optimization algorithm for Design of Digital Filters

Novel Teaching-learning-based Optimization Algorithm for Des...

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作者： Xiangyong Kong Zhinan Li Dexuan Zou Gai Pan School of Electrical Engineering & Automation Jiangsu Normal University Jiangsu Normal University Kewen College Zaozhuang Vocational College

An improved teaching-learning-based optimization algorithm named NTLBO is proposed for IIR digital design in this paper. Conventional mathematical methods have failed when reduced order adaptive models were used for the purposes of identification of problems. NTLBO utilizes a multi-learning strategy and opposition learning to overcome this disadvantage of the basic TLBO. The quasi-opposition-based learning has been applied to increase the diversity of solutions and broaden the search space to improve the global search ability. The multi-learning strategy makes local search more effective so as to speed up the convergence. To make a tradeoff between exploration and exploitation properly, the teaching factor is redesigned to increase the likelihood of solutions jumping out of local optima. Experiments are carried out on the classical examples and comparisons are made as well. The results indicate that the NTLBO algorithm achieved preferable performance in both reduced and same order models of ⅡR digital filters.

关键词： digital filters teaching-learning-based optimization algorithm random multi-learning strategy quasi-opposition learning

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optimization of thermal performance of a smooth flat-plate solar air heater using teaching-learning-based optimization algorithm

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COGENT ENGINEERING 2015年第1期2卷

作者： Rao, R. Venkata Waghmare, Gajanan SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching-learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.

关键词： smooth flat plate solar air heater thermal efficiency teaching-learning-based optimization algorithm

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DynTLBO - A teaching learning-based Dynamic optimization algorithm

DynTLBO - A Teaching Learning-based Dynamic Optimization Alg...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bari, A. T. M. Golam Gaspar, Alessio Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (纸本)9781509060177

The teaching learning based optimization (TLBO) algorithm simulates the knowledge-transfer process between teacher and learners as well as between peer learners. Although TLBO has been already successfully applied to both constrained and unconstrained engineering optimization problems, it sometimes prematurely converges toward local optima, especially in high dimensional, multimodal, or deceptive fitness landscapes. We therefore propose to further characterize the limitations of TLBO by investigating its performance on different benchmarks featuring both stationary, to establish a baseline, but especially non-stationary fitness landscapes. The results are then compared with a state of the art population-based optimization algorithm (Differential Evolution - DE) and its variants Self Adaptive Differential Evolution (jDE) in order to establish the suitability of TLBO on such landscapes. We found that TLBO exhibits a pronounced imbalance in its exploration vs. exploitation tradeoff that prevents it from maintaining a diversified population. It is well known that maintaining diversity as the population converges, and more generally balancing the exploration versus exploitation tradeoff, are both essential considerations in any population-based optimization technique. This is especially true for non-trivial problems where premature convergence to local optima is more likely. We therefore also proposed a novel TLBO variant that better manages population diversity and is therefore more suitable for dynamic optimization applications. We found that the resulting DynTLBO algorithm showed significant performance improvements on commonly used benchmarks(1).

关键词： teaching-learning-based optimization algorithm TLBO Population diversity Dynamic environment Search space exploration Search space exploitation

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A novel hybrid method based on teaching-learning algorithm and leader progression model for evaluating the lightning performance of launch sites and experimental tests

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ELECTRICAL ENGINEERING 2019年第2期101卷 619-633页

作者： Yahyaabadi, M. Aslani, F. Vahidi, B. Dept Power Generat Co Esfahan Iran Islamic Azad Univ Dept Elect Engn Khomeinishahr Branch Esfahan Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

In this article, a numerical optimization method has been used to study the lightning effect on satellite launch sites. In the conventional direct method, the leader progression model along with charge simulation method is employed for all the lightning leader tip positions and all the possible lightning current values on the equipment. This time-consuming calculation set is reduced in the developed method by using an optimization algorithm. For this purpose, the specifications of a real air termination system have been used for simulation and a system in a laboratory scales has been used for both purposes of simulation and practical tests. teaching-learning-based optimization (TLBO) algorithm has been employed as an intelligent method to find the number of lightning strikes to the equipment, to detect critical areas on the structure surface and to reduce the execution time. Also, some experimental tests conducted in a high-voltage laboratory are referred on the small-scale system to investigate the conditions of leaders inception and propagation. An appropriate adaptation between the simulation results of TLBO and conventional direct method verifies the application of this method to investigate the performance of other air termination systems.

关键词： Lightning Air terminals Leader progression model Number of direct lightning strokes teaching-learning-based optimization algorithm

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Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2012年第B6期226卷 1018-1025页

作者： Rao, Ravipudi V. Kalyankar, Vivek D. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

The concept of automation has been brought into the industries in order to increase the production rate and at the same time to minimize the production cost. The LBW process is widely replacing manual welding processes in many fabrication industries owing to the high level of automation. In the present work, an attempt is made to achieve conflicting objectives by finding optimum parameter settings for the LBW process. A recently developed advanced optimization algorithm is applied for parameter optimization of the LBW process. Two different multi-objective optimization examples are considered and significant improvement is obtained by the proposed optimization algorithm as compared with the earlier works.

关键词： Industrial laser beam welding process parameter optimization teaching-learning-based optimization algorithm

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A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第4期27卷 2962-+页

作者： Tefek, Mehmet Fatih Uguz, Harun Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol Kirsehir Turkey Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.

关键词： Hybrid optimization method gravitational search algorithm teaching-learning-based optimization algorithm economic dispatch power systems

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Optimal design of Stirling heat engine using an advanced optimization algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2016年第11期41卷 1321-1331页

作者： Rao, R. V. More, K. C. Taler, J. Oclon, P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India Politech Krakowska Inst Maszyn & Urzadzen Energetycznych Al Jana Pawla II 37 PL-31864 Krakow Poland

The Stirling engine presents an excellent theoretical output equivalent to the output of Carnot engine and it is less pollutant and requires little maintenance. In this paper, Stirling heat engine is considered for optimization with multiple criteria. A recently developed advanced optimization algorithm namely "teaching-learning-based optimization (TLBO) algorithm" is used for maximization of output power, minimization of pressure losses and maximization of the thermal efficiency of the entire solar Stirling system. The comparisons of the proposed algorithm are made with those obtained by using the decision-making methods like linear programming technique for multi-dimensional analysis of preference (LINMAP), technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy Bellman-Zadeh method that have used the Pareto frontier gained through non-dominated sorting genetic algorithm-II (NSGA-II). The comparisons were also made with those obtained by the experimental results. It is found that the TLBO algorithm has produced comparatively better results than those given by the decision-making methods and the experimental results presented by the previous researchers.

关键词： Stirling heat engine multi-objective optimization thermal efficiency pressure loss teaching-learning-based optimization algorithm

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Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第3期51卷 1855-1868页

作者： Tian, Guangdong Hao, Nannan Zhou, MengChu Pedrycz, Witold Zhang, Chaoyong Ma, Fangwu Li, Zhiwu Shandong Univ Sch Mech Engn Minist Educ Key Lab High Efficiency & Clean Mech Mfg Jinan 250061 Peoples R China Shandong Univ Natl Demonstrat Ctr Expt Mech Engn Educ Jinan 250061 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Tsinghua Univ Sch Ind Engn Beijing 100084 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2G7 Canada Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts' subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify lambda-fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among lambda-fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of lambda-fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.

关键词： Correlation Indexes Decision making Green design Safety Guidelines optimization Fuzzy Choquet integral grey comprehensive evaluation (GCE) multicriteria decision making (MCDM) teaching-learning-based optimization algorithm

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