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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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Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

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APPLIED COMPUTING AND INFORMATICS 2022年第1/2期18卷 151-162页

作者： Dash, Ch. Sanjeev Kumar Behera, Ajit Kumar Dehuri, Satchidananda Cho, Sung-Bae Silicon Inst Technol Bhubaneswar India Fakir Mohan Univ Dept Informat & Commun Technol Balasore India Yonsei Univ Dept Comp Sci Soft Comp Lab Seoul South Korea

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

关键词： Pattern recognition Imputation Classification Radial basis function neural networks teaching learning based optimization k-Nearest neighbor

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Multi-Objective parametric optimization during micro-EDM drilling of Ti-6Al-4 V using teaching learning based optimization algorithm

Multi-Objective parametric optimization during micro-EDM dri...

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International Conference on Advances in Materials and Mechanical Engineering. (ICAMME)

作者： Singh, Narender Bharti, Pushpendra S. GGS Indraprastha Univ Univ Sch Informat Commun & Technol Delhi 110078 India MSIT C-4 New Delhi 110058 India

The current paper investigates responses such as drilling rate (DR) and tool wear rate (TWR) experimentally for Electric discharge machining (EDM) during micro-hole drilling on titanium alloy with brass tool. The experiments are conducted using response surface methodology (RSM) based central composite design (CCD). Three process parameters like current, pulse-on-time and pulse-off-time are investigated for two responses: DR and TWR through experimentation. After conducting experiments, the effect of each parameter on responses has been found. Single and multi-objective optimization have been done using teaching learning based optimization (TLBO) algorithm to find the optimal combination of procedure parameters for maximum DR and minimum TWR. The confirmative test has been performed to verify the result of multi-objective optimization. A difference of<5% has been found between experimental and predicted values using the TLBO algorithm. The microstructure of the micro-drill machined surface is studied through SEM images. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Electrical Discharge Machining teaching learning based optimization Tool wear rate Drilling rate Central composite design

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Terminal voltage prediction of Li-Ion batteries using Combined Neural Network and teaching learning based optimization algorithm

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Narayanan, S. Siva Suriya Thangavel, S. Natl Inst Technol Puducherry Dept Elect & Elect Engn Karaikal India

The static and dynamic model parameters are critical parameters for the accurate estimation of opencircuit voltage and the terminal voltage of a Lithium-Ion (Li-Ion) battery. This work mainly focuses on the investigation of an accurate method for predicting the open-circuit voltage and terminal voltage of Li-Ion batteries. This work proposes an Enhanced Neural Network and teaching learning-based optimization (ENN-TLBO) algorithm to predict the terminal voltage of a Li-Ion battery used in electric vehicle applications. In this work, the static model is obtained by using a neural network (NN) while the dynamic model parameters are predicted by using the teaching learning based optimization technique (TLBO). In the NN method, different network parameters are varied to predict the accurate State of Charge (SoC)-Open Circuit Voltage (OCV) relation of the battery. The proposed static models are validated by the performance metrics such as RMSE and R2, the best static model is identified by low Root Mean Square Error (RMSE) and high R2 value of the model. In recent times many optimization algorithms are reported in the literature to predict the dynamic model, which requires certain algorithm-specific tuning parameters that affects the performance of the algorithm. The proposed TLBO algorithm is implemented with lesser tuning parameters to predict the hysteresis constant of the dynamic model. The RMSE value of the predicted terminal voltage at different temperature profiles fewer tuning parameters, simple to implement, and high accuracy.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Neural network activation function Machine learning (ML) Open Circuit Voltage (OCV) Battery terminal voltage teaching learning based optimization (TLBO) Training algorithm

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Design Technique of Open-Slot Surface-Mounted PM Machines Using Coenergy-based Subdomain Model and teaching-learning-based optimization Algorithm

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IEEE ACCESS 2024年 12卷 88492-88505页

作者： Lee, Do Hyeon Min, Seun Guy Soongsil Univ Dept Elect Engn Seoul 06978 South Korea

The open-slot stator topology, which allows for preformed coils and an excellent fill factor, has been regarded as a suitable candidate for low-speed operation such as direct-drive systems. This paper presents a rapid and accurate design optimization of surface-mounted permanent magnet (PM) machines equipped with the open-slot stator configuration. The analytical method, utilizing the coenergy-based subdomain model, is developed to predict the various electromagnetic performances. The proposed analytical model is decoupled into two components, relevant and irrelevant to the optimization process. Subsequently, the teaching-learning-based optimization (TLBO), a human-based algorithm for global solutions, is applied to the analytical model to search for the best compromise belonging to the Pareto-optimal front. The design methodology presented in this article is well-suited for the open-slot stator configuration, providing a computationally efficient and accurate approach to address multi-constrained minimization problems. In the end, the reliability of the proposed model is verified by finite element (FE) results.

关键词： optimization methods Analytical models Torque Power capacitors Electromagnetics Computational modeling Stator windings learning systems Permanent magnet machines coenergy principle direct drive open-slot stator optimization subdomain model surface-mounted permanent magnet teaching learning based optimization

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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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Parallel computing strategies for Sanitized teaching learning based optimization

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年 63卷

作者： Kommadath, Remya Maharana, Debasis Sivadurgaprasad, Chinta Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India Indian Inst Technol Guwahati Sch Business Gauhati 781039 Assam India

teaching-learning-based optimization is a metaheuristic technique that imitates the knowledge transfer in a classroom. The competitive performance with other state-of-the-art metaheuristic algorithms, innate simplicity, and the requirement of only two user-defined parameters are the primary reason behind the popularity of this algorithm. However, due to the inherent nature of the teaching learning based optimization, it is not amenable to parallel computing for the multiple evaluations of the fitness function, thereby limiting its application to computationally intensive problems. This article considers the teaching learning based optimization algorithm without the duplicate removal step as Sanitized teaching learning based optimization. In this work, three competitive parallelized strategies of Sanitized teaching learning based optimization are suggested that can harness the benefits of parallel computing. The efficacies of the proposed strategies are demonstrated on 14 benchmark problems and the CEC'14 real parameter benchmark test suite. Additionally, the impact on the computational time with respect to the computational complexity of the objective function and the number of resources has been studied. It is observed that the proposed strategies can lead up to a 90% reduction in computational time over Sanitized teaching learning based optimization algorithm.

关键词： Metaheuristic techniques teaching learning based optimization Sanitized teaching learning based optimization Parallel computing

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Modified teaching learning based optimization for selective harmonic elimination in multilevel inverters

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AIN SHAMS ENGINEERING JOURNAL 2022年第5期13卷

作者： Yaqoob, Muhammad Tayyab Rahmat, Mohd. Khairil Maharum, Siti Marwangi Mohamad Univ Kuala Lumpur Renewable Energy Res Lab RENERAL Elect Engn Sect British Malaysian Inst Gombak 53100 Selangor Malaysia Univ Kuala Lumpur Elect Technol Sect British Malaysian Inst Gombak 53100 Selangor Malaysia

Renewable Energy sources are becoming an important source of energy generation. Battery energy storages systems are used to supply the power when there is variation in power generation due to climate change. Power stored in batteries need to be converted from Direct current (DC) into Alternating current (AC) current to integrate it with the grids. This paper presents the modification in a recently emerged teaching learning-based optimization (TLBO) to be implemented in multilevel inverters. The results obtained from proposed modified TLBO has been compared with existing TLBO, Particle Swarm optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Imperialistic Competitive Algorithm (ICA). Furthermore, the proposed algorithm has been tested for four well-known mathematical functions like Greiwangks, Ackley's Dejon's and Drop wave and find out that the proposed algorithm has a better convergence rate than normal TLBO. Finally, the optimized switching angles obtained from the proposed algorithm has been tested in SIMULINK by designing an 11-level cascaded H-bridge multilevel inverter and observe the magnitude of individual harmonics. The results obtained from the proposed algorithm shows the better harmonic reduction while comparing with other algorithms. (c) 2022 THE AUTHORS. Published by Elsevier BV

关键词： Algorithm Power electronics teaching learning based optimization Selective harmonic elimination Multilevel inverters

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Multiple Flat Beams Generation Using Firefly and teaching learning based optimization Techniques 3rd

Multiple Flat Beams Generation Using Firefly and Teaching Le...

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3rd International Conference on Smart Computing and Informatics (SCI)

作者： Chaitanya, R. Krishna Rao, P. Mallikarjuna Raju, K. V. S. N. Raju, G. S. N. SRKR Engn Coll Dept ECE Bhimavaram India Andhra Univ Dept ECE Visakhapatnam Andhra Pradesh India Centurion Univ Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789813296909;9789813296893

Multiple flat beams have an extensive range of applications in mobile and wireless communications. They facilitate many users to connect simultaneously. Multiple beam pattern can be produced utilizing both conventional and modern optimization techniques. teaching learning based optimization and Firefly are applied for Multibeam purpose in this paper. Multibeam with a beamwidth of 0.2 (in U = sin.) each are produced. In the present work, unsymmetric array antenna is utilized to generate the Multibeam pattern. The beams are well-formed, and they are observed to be optimized. The patterns are presented in U domain. The resultant distribution of antenna parameters is found, with the goal that they can be used directly in Array antenna design.

关键词： Firefly algorithm Linear array antenna teaching learning based optimization Multiple flat beams

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A hybrid model using teaching-learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第9期12卷 8793-8808页

作者： Thawkar, Shankar Hindustan Coll Sci & Technol Dept Informat Technol Mathura Uttar Pradesh India

Feature selection is the most important step in the design of a breast cancer diagnosis system. The basic objective of the proposed methodology is to reduce the size of the feature space to improve the performance of the classification system. In this article, a hybrid teaching-learning based optimization (TLBO) with a Salp swarm algorithm (SSA) is presented to select the features with an artificial neural network as a fitness evaluator. The features selected by TLBO-SSA are evaluated using an adaptive neuro-fuzzy inference system. The performance of the proposed methodology is tested over 651 mammograms. The experimental results show that TLBO-SSA appears to be the best when compared with the basic TLBO algorithm. TLBO-SSA archived an accuracy of 98.46% with 98.81% sensitivity, 98.08% specificity, 0.9852 F-score, 0.9692 Cohen's kappa coefficient, and area under curve A(Z) = 0.997 +/- 0.001. Again the robustness of the proposed TLBO-SSA method is tested using a benchmark dataset obtained from the UCI repository. The result obtained by TLBO-SSA is compared with the Genetic Algorithm. The results show that TLBO-SSA is better than the Genetic Algorithm.

关键词： teaching learning based optimization Salp swarm algorithm Adaptive neuro-fuzzy inference system Artificial neural network Feature selection Mammography Classification

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