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A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 61卷

作者： Pan, Li Cheng, Wy-Liang Lim, Wei Hong Sharma, Abishek Jately, Vibhu Tiang, Sew Sun Alharbi, Amal H. El-kenawy, El-Sayed M. UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Zhengzhou Inst Engn & Technol Zhengzhou 450044 Peoples R China Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 India Univ Petr & Energy Studies Sch Engn Dept Elect & Elect Engn Dehra Dun 248007 India Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Bahrain Polytech Sch ICT Fac Engn Design & Informat & Commun Technol EDICT POB 33349 Isa Town Bahrain Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Jadara Univ Jadara Univ Res Ctr Irbid Jordan

Feature selection is a pivotal preprocessing step in deploying machine learning solutions, aimed at removing redundant features from datasets while preserving predictive accuracy. Despite the popular use of wrapper-based feature selection techniques with metaheuristic search algorithms (MSAs) such as teaching learning based optimization (TLBO), balancing exploration and exploitation in identifying optimal feature subsets remains a fundamental challenge. This paper introduces an advanced wrapper-based feature selection method utilizing an enhanced TLBO variant, termed Modified TLBO with Hierarchical learning Scheme (MTLBO-HLS). MTLBO-HLS incorporates significant enhancements in both teacher and learner phases to better balance exploration and exploitation, thus improving robustness in solving complex problems like feature selection. Specifically, the Multi-Hierarchical Teacher Phase (MH-TP) divides the MTLBO-HLS population into multiple group tiers based on fitness, where learners are guided by superior peers from better tiers. The Dynamic Peers Interaction based Learner Phase (DPI-LP) enriches the learning process by enabling interactions with single and multiple elite peers and retaining valuable knowledge across dimensions. Extensive simulations demonstrate MTLBO-HLS's superior performance in feature selection, consistently identifying the smallest feature subsets and achieving the highest classification accuracies across various datasets. These results highlight its potential to drive industrial innovation through enhanced machine learning model efficiency and accuracy.

关键词： Classification Dynamic Peers Interaction Feature Selection Hierarchical learning teaching learning based optimization

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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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teaching learning based optimization for economic load dispatch problem considering valve point loading effect

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 456-464页

作者： Banerjee, Sumit Maity, Deblina Chanda, Chandan Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 India Netaji Subhash Engn Coll Dept Elect Engn Kolkata 700152 India Indian Inst Engn Sci & Technol Dept Elect Engn Sibpur 711103 Howrah India

This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Economic load dispatch Genetic algorithm Particle swarm optimization teaching learning based optimization Valve point loading effect

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teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 10-19页

作者： Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India

This article presents a novel teaching learning based optimization (TLBO) to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants. TLBO is a recently developed evolutionary algorithm based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. The algorithm does not require any algorithm-specific parameters which makes the algorithm robust. Numerical results for two sample test systems are presented to demonstrate the capabilities of the proposed TLBO approach to generate optimal solutions of HTS problem. To test the effectiveness, three different cases namely, quadratic cost without prohibited discharge zones;quadratic cost with prohibited discharge zones and valve point loading with prohibited discharge zones are considered. The comparison with other well established techniques demonstrates the superiority of the proposed algorithm. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： teaching learning based optimization Hydrothermal scheduling Valve point effect Stochastic optimization Prohibited discharge zone

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teaching learning based optimization (TLBO) based Improved Iris Recognition System

Teaching Learning Based Optimization (TLBO) Based Improved I...

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23rd International Conference on Systems Engineering

作者： Agrawal, Shikha Sharma, Shraddha Silakari, Sanjay Rajiv Gandhi Proudyogiki Vishwavidyalaya UIT Dept Comp Sci & Engn Bhopal MP India

ISBN: (纸本)9783319084213

optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed teaching learning based optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods Standard Iris Recognition System and Genetic Algorithm optimized Iris Recognition System. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate. © Springer International Publishing Switzerland 2015.

关键词： Feature Extraction Gabor Wavelet Transform Genetic Algorithm Hough Transform Iris Recognition teaching learning based optimization

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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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Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization

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SCIENTIFIC REPORTS 2025年第1期15卷 1-34页

作者： Singh, Anurag Yadav, Shekhar Tiwari, Nitesh Nishad, Dinesh Kumar Khalid, Saifullah Madan Mohan Malaviya Univ Technol Dept Elect Engn Gorakhpur Uttar Pradesh India Dr Shakuntala Misra Natl Rehabil Univ Dept Elect Engn Lucknow India IBM Multi Act Co Ltd Khartoum Sudan

Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with teaching learning-based optimization (TLBO) for PID controller tuning in single-area LFC systems. Three reduction methods-Routh Approximation, Balanced Truncation, and Hankel Norm Approximation-were implemented to reduce system order from 4th to 2nd order, achieving a 47.3% reduction in computational time. The TLBO-optimized PID controller was compared with conventional tuning methods (Ziegler-Nichols, AMIGO, S-IMC, and CHR), demonstrating superior performance with a 38.2% decrease in settling time and 42.7% reduction in peak overshoot. The Routh Approximation method exhibited optimal performance with minimum settling time (2.8s) and peak overshoot (8.4%). Sensitivity analysis revealed stable system behavior with phase margin maintained at 84.25 degrees across parameter variations. The proposed approach achieved a 56.8% reduction in Integral Square Error compared to conventional methods, establishing its effectiveness for modern power grid applications. This research provides a robust framework for implementing efficient load frequency control in power systems while maintaining system stability and performance.

关键词： PID controller teaching learning based optimization Integral Square Error Load frequency control

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Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 66卷 67-77页

作者： Barisal, A. K. VSSUT Dept Elect Engn Burla Odisha India

This paper presents a new population based parameter free optimization algorithm as teaching learning based optimization (TLBO) and its application to automatic load frequency control (ALFC) of multi-source power system having thermal, hydro and gas power plants. The proposed method is based on the effect of the influence of teacher on the output of learners and the learners can enhance their knowledge by interactions among themselves in a class. In this extensive study, the algorithm is applied in multi area and multi-source realistic power system without and with DC link between two areas in order to tune the PID controller which is used for automatic generation control (AGC). The potential and effectiveness of the proposed algorithm is compared with that of differential evolution algorithm (DE) and optimal output feedback controller tuning performance for the same power systems. The dynamic performance of proposed controller is investigated by different cost functions like integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE) and integral of time multiplied absolute error (ITAE) and the robustness of the optimized controller is verified by its response toward changing in load and system parameters. It is found that the dynamic performance of the proposed controller is better than that of recently published DE optimized controller and optimal output feedback controller and also the proposed system is more robust and stable to wide changes in system loading, parameters, size and locations of step load perturbation and different cost functions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Automatic load frequency control Automatic generation control Dynamic performance Multi source power system HVDC link teaching learning based optimization

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Simultaneous allocation of distributed resources using improved teaching learning based optimization

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ENERGY CONVERSION AND MANAGEMENT 2015年 103卷 387-400页

作者： Kanwar, Neeraj Gupta, Nikhil Niazi, K. R. Swarnkar, Anil Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Taibah Univ Fac Engn Dept Elect Engn Madinah Saudi Arabia

Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of teaching learning based optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Distributed resources Meta-heuristic technique Intelligent search Smart distribution systems teaching learning based optimization

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Oppositional teaching learning based optimization approach for combined heat and power dispatch

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 57卷 392-403页

作者： Roy, Provas Kumar Paul, Chandan Sultana, Sneha Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This paper presents a new optimization technique i.e. teaching learning based optimization (TLBO) to solve combined heat and power dispatch (CHPD) problem with bounded feasible operating region. To accelerate the convergence speed and improve the simulation results, opposition based learning (OBL) is incorporated in basic TLBO algorithm. The potential of the proposed TLBO and oppositional TLBO (OTLBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for three different standard combined heat and power dispatch problems of power systems. The results of the proposed methods are compared with other popular optimization techniques like evolutionary programming (EP), three variants of particle swarm optimization (PSO), real coded genetic algorithm (RCGA), differential evolution (DE) and bee colony optimization (BCO). Through the simulation of MATLAB programming it is seen that OTLBO provides better results than all other optimization techniques at less computational time. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined heat and power dispatch Co-generation Evolutionary algorithm teaching learning based optimization Opposition based learning

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