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A New Variant of teaching learning based optimization Algorithm for Global optimization Problems

INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS 2019年第1期43卷 65-75页

作者： Kumar, Yugal Dahiya, Neeraj Malik, Sanjay Khatri, Savita JUIT Dept Comp Sci & Engn Solan Himachal Pardes India SRM Univ Dept Comp Sci & Engn Delhi NCR Campus Sonipat Haryana India BMIET Dept Comp Sci & Engn Sonepat India

This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.

关键词： teaching learning based optimization crossover meta-heuristics mutation

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teaching learning based optimization with Focused learning and its Performance on CEC2017 functions

Teaching Learning Based Optimization with Focused Learning a...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Kommadath, Remya Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati Assam India

ISBN: (纸本)9781509046010

In this work, we propose a variant to the teaching learning based optimization algorithm by incorporating focused learning of students. A student undergoes focused learning phase only when it is unable to obtain a better solution in the teacher phase and is expected to efficiently utilize the limited functional evaluations. The performance of this variant is evaluated on the single objective bound constrained real-parameter numerical optimization problems which have been proposed as a part of IEEE Congress on Evolutionary Computation. The proposed variant has provided competitive results in most of the problems.

关键词： teaching learning based optimization Focused learning Single objective optimization CEC 2017

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Application of teaching learning based optimization in Long term Optimal Reservoir Operation

Application of Teaching Learning Based Optimization in Long ...

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10th International Conference on Modelling, Identification and Control (ICMIC)

作者： You Wenxia Qiu Tianyi Li Wenwu Zhang Zhi China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China Guangdong Power Grid Corp Co Ltd Zhongshan Elect Power Supply Bur Guangzhou Guangdong Peoples R China State Gird DC Power Construct Branch Co Shanghai Peoples R China

ISBN: (纸本)9781538654163

Long term optimal reservoir operation is a nonlinear optimization problem. This paper adopts teaching learning based optimization (TLBO) to solve the Long term scheduling for reservoirs. Firstly, the basic test function is used to verify that the TLBO algorithm has high accuracy. Then the TLBO algorithm is applied to the Long term optimal reservoir operation model and the competitive selection and semi-feasible domain are introduced to deal with the constraint conditions. Comparing the TLBO algorithm with the genetic algorithm and the dynamic programming algorithm, solving the available TLBO algorithm has the advantages of high calculation accuracy, short computation time.

关键词： teaching learning based optimization Long term optimal reservoir operation competition selection semi - feasible domain

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teaching learning algorithm based optimization of kerf deviations in pulsed Nd:YAG laser cutting of Kevlar-29 composite laminates

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INFRARED PHYSICS & TECHNOLOGY 2018年 89卷 203-217页

作者： Gautam, Girish Dutt Pandey, Arun Kumar Jaypee Univ Engn & Technol Mech Engn Dept Guna MP India Bundelkhand Inst Engn & Technol Mech Engn Dept Jhansi UP India

Kevlar is the most popular aramid fiber and most commonly used in different technologically advanced industries for various applications. But the precise cutting of Kevlar composite laminates is a difficult task. The conventional cutting methods face various defects such as delamination, burr formation, fiber pullout with poor surface quality and their mechanical performance is greatly affected by these defects. The laser beam machining may be an alternative of the conventional cutting processes due to its non contact nature, requirement of low specific energy with higher production rate. But this process also faces some problems that may be minimized by operating the machine at optimum parameters levels. This research paper examines the effective utilization of the Nd:YAG laser cutting system on difficult-to-cut Kevlar-29 composite laminates. The objective of the proposed work is to find the optimum process parameters settings for getting the minimum kerf deviations at both sides. The experiments have been conducted on Kevlar-29 composite laminates having thickness 1.25 mm by using Box-Benkhen design with two center points. The experimental data have been used for the optimization by using the proposed methodology. For the optimization, teaching learning Algorithm based approach has been employed to obtain the minimum kerf deviation at bottom and top sides. A self coded Matlab program has been developed by using the proposed methodology and this program has been used for the optimization. Finally, the confirmation tests have been performed to compare the experimental and optimum results obtained by the proposed methodology. The comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach. Finally, the influence of different laser cutting parameters such as lamp current, pulse frequency, pulse width, compressed air pressure and cutting speed on top kerf deviation and bottom kerf devia

关键词： Kevlar composite Nd:YAG laser cutting Kerf deviation Multiple regression analysis teaching learning based optimization

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Intelligent Neighbourhood teaching learning based optimization Algorithm

Intelligent Neighbourhood Teaching Learning Based Optimizati...

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Singh, Geetanjali Sharma, Nirmala Sharma, Harish Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509064717

teaching learning based optimization Algorithm (TLBOA) is an efficient approach of dealing with linear, nonlinear and multidimensional optimization problems. It depicts the classroom behaviour of teachers and students (learners). The algorithm is divided in two phases, namely Teacher phase and Learner phase. The position update process of learner phase is based on the difference vector of two randomly selected solutions, which improves the diversification ability of the algorithm. Further, in case of high step size, the solutions may skip the global optima. Therefore, to improve the exploitation ability and to control the step size, a new phase called as intelligent phase inspired from the Onlooker bee phase of Artificial Bee Colony optimization algorithm, is incorporated with the TLBO. In the propounded phase, intelligent solutions will be given more chances in participation of the position update process. This will leads to high convergence in the swarm. The performance of this propounded algorithm is calculated over 15 benchmark functions and compared with other state of art optimization algorithms namely TLBO, Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO).

关键词： teaching learning based optimization Intelligent Phase Swarm Intelligence based algorithm optimization

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A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization

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APPLIED INTELLIGENCE 2016年第3期45卷 652-661页

作者： Goyal, Raman Kumar Kaushal, Sakshi Panjab Univ Univ Inst Engn & Technol Comp Sci & Engn Chandigarh 160014 India

Non-linear optimization models have been recently proposed to derive crisp weights from fuzzy pairwise comparison matrices. In this paper, a TLBO (teaching learning based optimization) based solution is presented for solving an optimization model as a system of non-linear equations to derive crisp weights from fuzzy pairwise comparison matrices in AHP (Analytic Hierarchy Process). This fuzzy-AHP method is named as TLBO-1. It has been found that TLBO-1 can lead to inconsistent or less consistent weights. To solve the problem of inconsistent weights, a new constrained non-linear optimization model is proposed in this paper. This model is based on the min-max approach for fuzzy pairwise comparison ratios of weights. TLBO is again used to solve this optimization model, and crisp weights are derived. This fuzzy AHP method is named as TLBO-2. The effectiveness of the proposed model is illustrated by three examples. For each example, the consistency of the derived crisp weights is compared with other optimization models. The results show that the TLBO-2 method can derive more consistent weights for the fuzzy AHP based Multi-Criteria Decision Making (MCDM) systems as compared to the other optimization models.

关键词： Multi-criteria decision making Fuzzy analytic hierarchy process teaching learning based optimization Comparison matrices Priority weights

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Parameters identification of solar cell models using generalized oppositional teaching learning based optimization

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ENERGY 2016年 99卷 170-180页

作者： Chen, Xu Yu, Kunjie Du, Wenli Zhao, Wenxiang Liu, Guohai Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

This paper presents a new optimization method called GOTLBO (generalized oppositional teaching learning based optimization) to identify parameters of solar cell models. GOTLBO employs generalized opposition-based learning to basic teaching learning based optimization through the initialization step and generation jumping so that the convergence speed is enhanced. The performance of GOTLBO is comprehensively evaluated in thirteen benchmark functions and two parameter identification problems of solar cell models, i.e., single diode model and double diode model. Simulation results indicate the excellent performance of GOTLBO compared with four well-known evolutionary algorithms and other parameter extraction techniques proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Solar cell models Parameter identification teaching learning based optimization Generalized opposition-based learning

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Optimal proton exchange membrane fuel cell modelling based on hybrid teaching learning based optimization - Differential Evolution algorithm

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AIN SHAMS ENGINEERING JOURNAL 2016年第1期7卷 347-360页

作者： Turgut, Oguz Emrah Coban, Mustafa Turhan Ege Univ Dept Mech Engn Fac Engn Izmir Turkey

Simulation proton exchange membrane fuel cell (PEMFC) performance accurately is a challenging process. Many mathematical models have been existed, yet due to lack of accurate parameter estimations, considerable errors might occur. Nowadays, meta-heuristic optimization algorithms have been successfully applied for parameter identification of PEMFC models. In this study, teaching learning based optimization method (TLBO) is hybridized with Differential Evolution (DE) algorithm for successful estimation of unknown PEMFC model parameters. Efficiency of the proposed algorithm is tested with several benchmark problems. A case study taken from the literature has been performed by hybrid TLBO-DE algorithm and other optimization methods such as Melody Search (MS), Backtracking Search (BS), Artificial Cooperative Search (ACS), Quantum behaved Particle Swarm optimization (QPSO), Bat algorithm (BAT), Intelligent Tuned Harmony Search (ITHS) and Cuckoo Search (CS). TLBO-DE algorithm surpasses all these optimizers in terms of solution quality and accuracy. (C) 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： teaching learning based optimization Differential Evolution Parameter identification Polarization curve Proton exchange membrane fuel cell

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Optimal Combined Overcurrent and Distance Relays Coordination using teaching learning based optimization

Optimal Combined Overcurrent and Distance Relays Coordinatio...

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IEEE India Council International Conference

作者： Saptarshi Roy P. Suresh Babu N. V. Phanendra Babu National Institute of Technology

ISBN: (纸本)9781538643198

Relay coordination is an important aspect to maintain proper power system operation and control. Relays should be organized in such a way that every relay should have a backup and Coordination time interval (CTI) between primary and back up and different zones of the relay should be maintained to achieve proper fault identification and fault clearance sequence. The relays should operate in minimum desirable time satisfying all the co-ordination constraints. So, relay coordination is nothing but highly constraint problem. Heuristic techniques are often used to get optimal solution of this kind of problem. In this paper this constraint problem is solved by teaching learning based optimization TLBO) on a WSCC-3-Machine-9bus system. Proper desirable time setting multiplier (TSM) with minimum operating time of relays are calculated. We also incorporated intelligent over current relay characteristics selection to get the desired results in this work. The results seem to be satisfactory as the results obtained from TLBO are comparatively better than so called conventional methods like Genetic Algorithm(GA) and Particle Swarm optimization (PSO).

关键词： Coordination of relay Coordination time interval teaching learning based optimization Plug setting Time setting multiplier Over current relay characteristics

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teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 43卷 89-101页

作者： Chaves-Gonzalez, Jose M. Perez-Toledano, Miguel A. Navasa, Amparo Univ Extremadura Dept Comp Sci Caceres Spain

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer's satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Software requirements selection Multi-objective evolutionary algorithm teaching learning based optimization Search based Software Engineering Next Release Problem Swarm intelligence

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