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A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)

INFORMATION SCIENCES 2016年 357卷 182-200页

作者： Patel, Vivek K. Savsani, Vimal J. Gujarat Technol Univ Ahmadabad Gujarat India Pandit Deendayal Petr Univ Gandhinagar 382007 Gujarat India

This paper presents an efficient multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The proposed algorithm uses a grid-based approach in order to keep diversity in the external archive. Pareto dominance is incorporated into the MO-ITLBO algorithm in order to allow this heuristic to handle problems with several objective functions. The qualities of the solution are computed based on the Pareto dominance notion. The performance of the MO-ITLBO algorithm is assessed by applying it on a set of standard test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The results obtained using the proposed algorithm is compared with the other state-of-the-art algorithms available in the literature. Moreover, the performance of the MO-ITLBO algorithm is also compared with the multi-objective version of the basic teaching-learning based optimization algorithm (MO-TLBO). The statistical analysis of the experimental work is also carried out by conducting Friedman's rank test and Holm post hoc procedure. The results show that the proposed approach is competitive and effective compared to other algorithms contemplated in this work and it can also find the result with greater precision. (C) 2014 Elsevier Inc. All rights reserved.

关键词： teaching learning based optimization Improved teaching learning based optimization Multi-objective optimization Inverted generational distance

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Power Quality Improvement with Shunt Active Filter under Various Mains Voltage Using teaching learning based optimization 2

Power Quality Improvement with Shunt Active Filter under Var...

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2nd International Conference on Recent Advances in Engineering

作者： Mishra, Soumya Ray, Pravat Kumar Natl Inst Technol Dept Elect Engn Rourkela 769008 India

ISBN: (纸本)9781467382533

In this paper, instantaneous reactive power theory based a modified control algorithm is implemented for 3-phase 3-wire shunt active power filter to suppress harmonics and reactive power under non ideal source voltage. The reference current signals are generated by sensing the source voltage, load current and dc bus voltage. With these signals the gate driving pulses are generated via hysteresis band current controller. To check robustness of the control algorithm four different cases such as unbalanced, balanced distorted, unbalanced distorted main voltage and faults on load are considered. For more improvement in the performance of active filter, the Active filter is optimized with teaching-learning-based optimization. The simulation work is carried out in MA TLAB/SIMULINK Power system block set. The performance of the implemented algorithm with teaching-learning-based optimization is found quite effective and satisfactory under these conditions.

关键词： Shunt active power filter teaching learning based optimization Total harmonic distortion Instantaneous power theory

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Comparative Performance Evaluation of teaching learning based optimization against Genetic Algorithm on Benchmark Functions

Comparative Performance Evaluation of Teaching Learning Base...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Ebraheem, M. Jyothsna, T. R. GITAM Univ GITAM Inst Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India AU Coll Engn Dept Elect Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781479974559

In this paper effectiveness of newly introduced 'teaching learning based optimization' (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.

关键词： teaching learning based optimization Genetic Algorithm Benchmark functions

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GPGPU based teaching learning based optimization and Artificial Bee Colony Algorithm for Unconstrained optimization Problems 5

GPGPU based Teaching Learning based Optimization and Artific...

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IEEE International Advance Computing Conference (IACC 2015)

作者： Mane, Sandeep U. Omane, Rajshree Pawar, Aprupa Rajarambapu Inst Technol Dept CSE Rajaramnagar India

ISBN: (纸本)9781479980475

teaching learning based optimization (TLBO) and Artificial bee colony (ABC) algorithm is population based modern method of optimization, used to solve diverse complex engineering and real time applications. To obtain best solution for the complex problem it requires more time and results in performance degradation. To improve the performance of the population based algorithm, they are either parallelized or implemented on General Purpose Graphic Processing Unit (GPGPU). In this paper, the GPGPU based implementation of TLBO and ABC algorithm is discussed to solve unconstrained benchmark problems. The performance of both the approaches is compared based on standard deviation, standard error mean and time. It is observed that both the approaches gives good results but time taken by TLBO algorithm is more as compared to ABC algorithm.

关键词： Artificial bee colony teaching learning based optimization Unconstrained optimization problem General Purpose Graphic Processing Unit

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Optimal Location and Sizing of Distributed Generation Units for Maximum Loss Reduction using teaching learning based optimization through Matlab GUI

Optimal Location and Sizing of Distributed Generation Units ...

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International Conference on Electrical, Electronics, Signals, Communication and optimization (EESCO)

作者： Harini, M. Reddy, M. Damodar SV Univ Coll Engn EEE Dept Tirupati Andhra Pradesh India

ISBN: (纸本)9781479976782

Optimal placement of distributed generation in a distribution system is a problem which involves the optimization of both discrete and continuous variables. In radial distribution network, distributed generation (DG) plays a crucial role in satisfying the demands of electrical energy. It is a small generating plant which supports the distribution network by minimizing system power losses, improving the stability and voltage of the network. In this paper, teaching learning based optimization (TLBO) technique is used for finding optimal DG locations and sizes simultaneously. The main goal is to achieve substantial improvement in the voltage profile and significant reduction in network power losses. The TLBO is inspired by the influence of the teaching by the teacher on the students in a class. In this paper, GUI is developed in Matlab to perform load flow and to also obtain optimal locations and capacities of DG units. The proposed method was tested on IEEE 15 bus, 33 bus and 69 bus systems and the results were discussed.

关键词： Distributed Generation teaching learning based optimization Voltage Profile Loss Reduction Graphical User Interface

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Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective teaching learning based optimization Algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第3期12卷 656-666页

作者： Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Rubio-Largo, Alvaro Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

关键词： teaching learning based optimization hybrid algorithm multiobjective optimization proteins PROSITE

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Design of Controller for Automatic Voltage Regulator Using teaching learning based optimization

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Procedia Technology 2015年 21卷 295-302页

作者： V. Rajinikanth Suresh Chandra Satapathy Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering Chennai 600119 India Department of Computer Science and Engineering ANITS Visakhapatnam 531162 India.

In this paper, One Degree Of Freedom (1DOF) and Two Degrees Of Freedom (2DOF) Proportional + Integral + Derivative (PID) controller design is proposed and implemented on the Automatic Voltage Regulator (AVR) system using traditional teaching learning based optimization (TLBO) algorithm. Minimization of a multi-objective function guides the TLBO algorithm's exploration until the process converges with an optimal solution. A simulation study is carried to examine the performance of TLBO assisted controller design procedure for three, four and five dimensional searches. The performance of the proposed method is validated with most successful heuristic procedures, such as Particle Swarm optimization (PSO), Bacterial Foraging optimization (BFO) and Firefly Algorithm (FA). The result show that, 1DOF PID controller and PID controller with filter offers smooth reference tracking response and the 2DOF PID controller with the Feed Forward (FF) and Feed Back (FB) structure presents reduced time domain and error values compared to the alternatives.

关键词： AVR PID controller degrees of freedom teaching learning based optimization multi-objective function

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Multi-level production planning in a petrochemical industry using elitist teaching-learning-based-optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第1期42卷 628-641页

作者： Kadambur, Rajasekhar Kotecha, Prakash Indian Inst Technol Dept Chem Engn Gauhati 781039 Assam India

The complex nature of the petrochemical industries necessitates an efficient decision on a large number of factors so as to optimally operate a plant. Production planning is an integral part of the petrochemical industry and requires the optimal selection of processes, production levels and products to maximize its profit. Previously an MILP formulation has been proposed for guiding the petrochemical industry development in Saudi Arabia (Alfares & Al-Amer, 2002). In this article, we state the limitations of this formulation and propose an alternate elitist TLBO algorithm based strategy to overcome them. The benefits of this strategy include the determination of better production plans that lead to higher profits and have been demonstrated on the eight case studies in the literature. The proposed strategy is generic and can be applied to determine production plans of multiple levels in various industries. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Combinatorial optimization Production planning teaching learning based optimization

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Power Quality Improvement with Shunt Active Filter under Various Mains Voltage Using teaching learning based optimization

Power Quality Improvement with Shunt Active Filter under Var...

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International Conference on Recent Advances in Engineering & Computational Sciences

作者： Soumya Mishra Pravat Kumar Ray Department of Electrical Enggineering National Institute of Technology

ISBN: (纸本)9781467382540

In this paper, instantaneous reactive power theory based a modified control algorithm is implemented for 3-phase 3-wire shunt active power filter to suppress harmonics and reactive power under non ideal source voltage. The reference current signals are generated by sensing the source voltage, load current and dc bus voltage. With these signals the gate driving pulses are generated via hysteresis band current controller. To check robustness of the control algorithm four different cases such as unbalanced, balanced distorted, unbalanced distorted main voltage and faults on load are considered. For more improvement in the performance of active filter, the Active filter is optimized with teaching-learning-based optimization. The simulation work is carried out in MATLAB/SIMULINK Power system block set. The performance of the implemented algorithm with teaching-learning-based optimization is found quite effective and satisfactory under these conditions.

关键词： Shunt active power filter teaching learning based optimization Total harmonic distortion Instantaneous power theory

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teaching-learning based optimization with global crossover for global optimization problems

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APPLIED MATHEMATICS AND COMPUTATION 2015年 265卷 533-556页

作者： Ouyang, Hai-bin Gao, Li-qun Kong, Xiang-yong Zou, De-xuan Li, Steven Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne Vic 3000 Australia

teaching learning based optimization (TLBO) is a newly developed population based meta heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： teaching learning based optimization Global optimization Crossover

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