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Design optimization of a compressor transition S-shaped duct using a teaching-learning-based optimization algorithm

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2019年第12期41卷 1-18页

作者： Sharma, Manish Baloni, Beena D. Sardar Vallabhbhai Natl Inst Technol Dept Mech Engn Surat 395007 India

A high bypass turbofan aero-engine delivers compressed air from the low-pressure to the high-pressure compressor through a compressor transition duct. Weight and design space limitations impel to its S-shaped design. Despite that, the compressor transition duct has to guide the flow very carefully to the high-pressure compressor without disturbances and flow separations. Hence, the present paper is devoted to elaborate on the application of the proposed heuristic optimization technique known as multi-objective teaching-learning-based optimization (MO-TLBO) algorithm in the area of S-shaped transition compressor duct. The present algorithm is applied for obtaining the optimum design of the S-shaped duct while minimizing the total pressure loss coefficient and non-uniformity index at the S-shaped duct outlet. These objectives are also optimized independently and as a multi-objective consideration as well. Because of the conflicting nature of the objective functions, a Pareto-optimal curve is also presented for a successful trade-off between the objectives. Pareto-optimal curve gives flexibility to the designer for selecting the best set of the parameters based on the objectives. Successful execution of the proposed MO-TLBO algorithm is examined by comparing the optimum results acquired by the multi-objective genetic algorithm and comparisons divulge that the TLBO algorithm can be successfully practiced to obtain the optimized design of compressor transition S-shaped duct. Optimized duct shows the reduction in total pressure loss and non-uniformity by 28.80% and 36.67%, respectively, despite the reduction of 14.74% in overall length.

关键词： Transition S-shaped duct Total pressure loss coefficient Non-uniformity index teaching-learning-based optimization algorithm Pareto-optimal front

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Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis

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JOURNAL OF SOUND AND VIBRATION 2019年 448卷 230-246页

作者： Khatir, Samir Wahab, Magd Abdel Boutchicha, Djilali Khatir, Tawfiq Univ Ghent Fac Engn & Architecture Dept Elect Energy Met Mech Construct & Syst Ghent Belgium Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Univ Ghent Fac Engn & Architecture Soete Lab Technol Pk Zwijnaarde 903 B-9052 Zwijnaarde Belgium Univ Sci & Technol Oran Mohamed Boudiaf Lab Mecan Appl Bir El Djir Algeria Univ Ctr Salhi Ahmed Inst Sci & Technol Naama Algeria

This paper proposes a two-stage approach for damage assessment in beam-like structures using two-dimensional Isogeometric Analysis (IGA) and Finite Element Method (FEM) combined with optimization techniques. In the first stage, the Local Frequencies Change Ratio (LFCR) indicator and a newly developed damage indicator based on normalized Modal Strain Energy Indicator (nMSEDI) are introduced to locate effectively the potential damaged elements. In order to verify nMSEDI, different scenarios based on single and multiple damages are studied using numerical experiments. In the second stage, the teaching-learning-based optimization algorithm (TLBO) is utilized and its performance is compared with that of Particle Swarm optimization (PSO) and Bat algorithm (BA). The three optimizations techniques are combined with IGA using nMSEDI as objective function. In addition, experimental vibration tests using laboratory steel been are conducted to validate the proposed technique. The obtained results clearly indicate that the proposed approach can be used to determine accurately and efficiently both damage location and severity in beam-like structures. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Inverse problem Experimental modal analysis Isogeometric analysis Finite element method optimizations teaching-learning-based optimization algorithm

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Improved teaching-learning-based optimization algorithm and its Application in PID Parameter optimization

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2019年第2期13卷 1-17页

作者： Gu, Fahui Wang, Wenxiang Lai, Luyan Jiangxi Appl Technol Vocat Coll Nanchang Jiangxi Peoples R China Jiangxi Environm Engn Vocat Coll Nanchang Jiangxi Peoples R China

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.

关键词： Dual-Population Co-Evolution teaching learning optimization algorithm Proportion Integration Differentiation teaching-learning-based optimization algorithm

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Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching-learning-based optimization algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第8期29卷 1715-1737页

作者： Rao, R. Venkata Rai, Dhiraj P. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India Univ Maribor Maribor Slovenia

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting teaching-learning-based optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.

关键词： Sustainable machining processes Micro-machining Parameter optimization teaching-learning-based optimization algorithm A posteriori approach

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Multiobjective optimization of in situ process parameters in preparation of Al-4.5%Cu-TiC MMC using a grey relation based teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2018年第4期232卷 393-407页

作者： Das, Biswajit Roy, Susmita Rai, R. N. Saha, S. C. Natl Inst Technol Dept Mech Engn Jirania 799046 Tripura India Natl Inst Technol Dept Math Jirania Tripura India Natl Inst Technol Dept Prod Engn Jirania Tripura India

In modern in situ composite fabrication processes, the selection of optimal process parameters is greatly important for the preparation of best quality metal matrix composite. For achieving high-quality composite, an efficient optimization technique is essential. The present study explores the potential of a new robust algorithm named teaching-learning-based optimization algorithm for in situ process parameter optimization problems in fabrication of Al-4.5%Cu-TiC metal matrix composite fabricated by stir casting technique. optimization process is carried out for optimizing the in situ processing parameters i.e. pouring temperature, stirring speed, reaction time for achieving better mechanical properties, i.e. better microhardness, toughness, and ultimate tensile strength. Taguchi's L-25 orthogonal array design of experiment was used for performing the experiments. Grey relational analysis is used for the conversion of the multiobjective function into a single objective function, which is being used as the objective function in the teaching-learning-based optimization algorithm. Confirmation test results show that the developed teaching-learning-based optimization model is a very efficient and robust approach for engineering materials process parameter optimization problems.

关键词： teaching-learning-based optimization algorithm in-situ process metal matrix composite mechanical properties optimization

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Comparison of stationary and rotary matrix heat exchangers using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2018年第4期232卷 493-502页

作者： Hajabdollahi, Hassan Vali E Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran

In this paper, two kinds of compact heat exchanger including plate fin heat exchanger and rotary regenerator, respectively the stationary and rotary matrix heat exchanger, are compared. For this purpose, both heat exchangers are optimized by considering three simultaneous objective functions including effectiveness, heat exchanger volume, and total pressure drop using multi-objective teaching learning based optimization algorithm. Six different design parameters are considered for the both plate fin heat exchanger and rotary regenerator. optimization is performed for the same and different hot and cold side mass flow rates. The optimum results reveal 13.26% growth in the effectiveness, 475.17% increase in the volume, and 95.45% reduction in the pressure drop in RR as compared with plate fin heat exchanger and for the final optimum point. As a result, rotary regenerator is more suitable in the case of high effectiveness and low pressure drop while plate fin heat exchanger is more suitable in the case of space limitation (lower heat exchanger volume).

关键词： Plate fine heat exchanger rotary regenerator objective functions teaching-learning-based optimization algorithm

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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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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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An Improved teaching-learning-based optimization algorithm to Solve Job Shop Scheduling Problems 16

An Improved Teaching-Learning-Based Optimization Algorithm t...

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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Li, Linna Weng, Wei Fujimura, Shigeru Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781509055074

Job shop scheduling problem (JSP) is a strongly NP-hard combinatorial optimization problem. It is difficult to solve the problem to the optimum in a reasonable time. teaching-learning-based optimization (TLBO) algorithm is a novel population oriented meta-heuristic algorithm. It has been proved that TLBO has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. In this paper, the traditional TLBO is improved to enhance diversification and intensification when exploring solutions for JSP. The improvements include changing the coding method, increasing number of teachers, introducing new learners and performing local search around potentially optimal solutions. To show effectiveness of the improved TLBO algorithm, the simulation results obtained by the improved TLBO for benchmark problems are compared with results obtained by the traditional TLBO and the best known lower bounds.

关键词： job shop scheduling teaching-learning-based optimization algorithm optimization

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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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