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A novel hybrid teaching learning based multi-objective particle swarm optimization

NEUROCOMPUTING 2017年 222卷 11-25页

作者： Cheng, Tingli Chen, Minyou Fleming, Peter J. Yang, Zhile Gan, Shaojun Chongqing Univ Sch Elect Engn Chongqing Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to promote the diversity and improve search ability. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence.

关键词： Multi-objective optimization Particle swarm optimization teaching learning based optimization Crowded sorting

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optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study

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FRONTIERS IN MATERIALS 2024年 10卷

作者： Jatti, Vijaykumar S. Tamboli, Shahid Shaikh, Sarfaraj Solke, Nitin S. Gulia, Vikas Jatti, Vinaykumar S. Khedkar, Nitin K. Salunkhe, Sachin Pagac, Marek Nasr, Emad S. Abouel Symbiosis Inst Technol Dept Mech Engn Pune India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India Gazi Univ Fac Engn Dept Mech Engn Ankara Turkiye Fac Mech Engn Dept Machining Assembly & Engn Technol Ostrava Czech Republic King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia

This research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile strength of the samples was determined in compliance with ASTM D638 standard, and the experiments were carried out according to a planned arrangement. Six distinct methods were used to optimize the tensile strength: Particle Swarm optimization (PSO), teaching learning based optimization (TLBO), Genetic Algorithm (GA), Simulated Annealing (SA), and Cohort Intelligence (CI). Several runs of the optimization methods demonstrated their consistency in producing the same values of tensile strength, indicating their reliability. The optimization results showed that JAYA performed better than the other algorithms, resulting in a material with the maximum tensile strength of 55.475 N/mm2. Validation experiments were carried out to confirm the efficacy of these algorithms. The results showed that the ideal input parameters produced tensile strength values that closely matched the anticipated values with a low percentage error. The benefits of applying these algorithms to improve the tensile strength of PLA materials for 3D printing are demonstrated by this study, which also offers insightful information about how to optimize FDM procedures.

关键词： tensile strength fused deposition modeling JAYA algorithm teaching learning based optimization particle swarm optimization cohort intelligence

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Heuristic Model to Compute Indices for Classification of Incidence and Non-Incidence of Thunderstorms Over Ranchi With Atmospheric Parameter

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IEEE ACCESS 2021年 9卷 127086-127101页

作者： Bala, Kanchan Paul, Sanchita Ghosh, Mili Birla Inst Technol Mesra Deptt Comp Sci & Engn Ranchi 835215 Bihar India Birla Inst Technol Mesra Dept Remote Sensing Ranchi 835215 Bihar India

Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the teaching learning based optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.

关键词： Mathematical model Storms Atmospheric modeling Monsoons Indexes Meteorology Lightning Correlation coefficient thunderstorm teaching learning based optimization differential evolution simulated annealing

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3D affine registration using teaching-learning based optimization

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3D RESEARCH 2013年第3期4卷 2-2页

作者： Jani, Ashish Savsani, Vimal Pandya, Abhijit Florida Atlantic Univ Boca Raton FL 33431 USA Pandit Deendayal Petr Univ Gandhinagar Gujarat India

3D image registration is an emerging research field in the study of computer vision. In this paper, two effective global optimization methods are considered for the 3D registration of point clouds. Experiments were conducted by applying each algorithm and their performance was evaluated with respect to rigidity, similarity and affine transformations. Comparison of algorithms and its effectiveness was tested for the average performance to find the global solution for minimizing the error in the terms of distance between the model cloud and the data cloud. The parameters for the transformation matrix were considered as the design variables. Further comparisons of the considered methods were done for the computational effort, computational time and the convergence of the algorithm. The results reveal that the use of TLBO was outstanding for image processing application involving 3D registration.

关键词： optimization image registration teaching learning based optimization simulated annealing iterative closed point

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Enhancement of PV performance by using hybrid TLBO-EO optimization

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AIN SHAMS ENGINEERING JOURNAL 2023年第3期14卷

作者： Sayed, Eman A. Sameh, Mariam A. Attia, Mahmoud A. Badr, Ahmed O. Ain Shams Univ Elect Power & Machines Dept Cairo Egypt Future Univ Egypt Elect Engn Dept Cairo Egypt

Photovoltaic systems are considered among the most promising renewable energy sources. This paper proposes a relatively new hybrid optimization technique to tune the PV system controller gains under normal conditions and dynamic irradiance. The introduced hybrid optimization technique combines both the teaching learning based optimization (TLBO) which has high exploration and the Equilibrium Optimizer (EO) which has high exploitation. Moreover, a comparison with various optimization approaches is presented to validate the efficacy of the proposed approach. The superiority of the proposed hybrid optimization technique is demonstrated in improving the PV system performance compared to other previously reported algorithms.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： Renewable energy optimization control PV System Performance enhancement teaching learning based optimization (TLBO) Equilibrium Optimizer (EO) PI controller

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Evaluation of SFLA and TLBO Algorithm for Path Planning of Mobile Robots in MATLAB 1

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1st International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020

作者： Agarwal, Divya Bharti, Pushpendra S. Guru Gobind Singh Indraprastha University New Delhi110078 India

ISBN: (数字)9789811627941

ISBN: (纸本)9789811627934

Autonomous robots are employed in manufacturing units and industries as they can perform complex tasks in hazardous and harmful environments such as space explorations, submarines, and mining work. When a robot can traverse from start to goal location while evading obstacles, it is called an autonomous robot for which path planning is essential. All evolutionary and swarm intelligence approaches are probabilistic-based and entail few to many control parameters based on problem and application area. Shuffled frog leaping algorithm (SFLA) is one such example that requires six problem-specific parameters, while teaching learning-based optimization (TLBO) is a specific parameter-less algorithm. Thus, there is no burden of tuning control parameters in TLBO. In this work, comparison of both these algorithms is done on MATLAB 2018a to demonstrate the results that despite the usage of no specific parameters, TLBO outperforms SFLA algorithm for path planning and obstacle avoidance problem (PPOA). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle avoidance Path planning Shuffled frog leaping algorithm teaching learning based optimization

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An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

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HELIYON 2024年第5期10卷 e26799页

作者： Khanna, Munish Singh, Law Kumar Shrivastava, Kapil Singh, Rekha Galgotias Univ Sch Comp Sci & Engn Greater Noida Gautam Buddh Na India GLA Univ Dept Comp Engn & Applicat Mathura India Uttar Pradesh Rajarshi Tandon Open Univ Dept Phys Prayagraj Uttar Pradesh India

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely teaching learning-based optimization (TLBO), Elephant Herding optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed;however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based

关键词： Feature selection Breast cancer prediction Machine learning Soft-computing teaching learning based optimization Elephant herding optimization Hybrid approach

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Available Transfer Capability Enhancement in Deregulated Power System through TLBO Optimised TCSC

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ENERGIES 2022年第12期15卷 4448页

作者： Gautam, Anurag Ibraheem Sharma, Gulshan Bokoro, Pitshou N. Ahmer, Mohammad F. Jamia Millia Islamia Elect Engn Dept New Delhi 110025 India Univ Johannesburg Dept Elect Engn Technol ZA-2006 Johannesburg South Africa Mewat Engn Coll Dept Elect & Elect Engn Nuh 122107 India

Rapid industrial development and innovations in technology bring about the menace of congestion in deregulated power systems (DPS). The transmission lines are continuously working under a stressed condition with reduced power transfer capacity. In this situation, the power losses and voltage deviations at the load buses are increased and hence reduce the system stability. To mitigate congestion, improving available transfer capability (ATC) of the transmission system is one of the most feasible and practical solution. This paper focuses on the implementation of Thyristor Controlled Series capacitor (TCSC) to mitigate congestion by enhancing ATC and via reducing power losses. AC Power Transfer Distribution Factor (ACPTDF) is applied to calculate ATC and to find the location of TCSC. To optimize the TCSC parameter (reactance), a teaching learning based optimization (TLBO) is proposed in the present work. The proposed optimization is validated on the IEEE 30 Bus system. The results are validated by matching with the results obtained through standard grey wolf optimization (GWO) and particle swarm optimization (PSO) techniques. The results show that despite two antagonistic objectives of ATC enhancement and power loss reduction, TLBO outperformed the other optimization techniques under different contingency conditions. The overall ATC of the IEEE 30 Bus system for the bilateral transactions is enhanced by 11.86%. Further active and reactive power losses are reduced by 16.7% and 29.6% for DPS.

关键词： available transfer capacity deregulated power system TCSC teaching learning based optimization congestion ac power transfer distribution factor

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LSMA-TLBO: A hybrid SMA-TLBO algorithm with levy flight based mutation for numerical optimization and engineering design problems

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期172卷

作者： Kundu, Tanmay Garg, Harish Chandigarh Univ Dept Math Mohali 140413 Punjab India Deemed Univ Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

The objective of this paper is to present a novel hybrid algorithm LSMA-TLBO for solving the numerical and engineering design optimization problems. The proposed algorithm utilizes the features of Slime Mould Algorithm (SMA) and the teaching-learning based optimization (TLBO) to balance the exploitation and exploration ability. In addition, a levy flight based mutation is introduced in this study to maximize the exploration ability of the algorithm. SMA is one of the meta-heuristic algorithms (MHAs) which mimics the oscillation mechanism of slime mould in nature. It is an efficient technique that has been successfully applied to the complex optimization problems (OPs) due to its global exploration ability. However, despite its popularity, the SMA also suffers from some shortcomings, such as local optima stagnation, slow convergence rate and improper balance between exploitation and exploration. In view of this weaknesses, the hybrid algorithm LSMA-TLBO is proposed considering the good global search ability of SMA, fast convergence of TLBO and the features of the levy flight based mutation to maximize the exploration ability. The performance of the proposed LSMA-TLBO is evaluated on 23 different benchmark functions including uni-modal and multi-modal problems. In addition, IEEE CEC-C06 2019 test suite and six engineering design problems are also considered to examine the practicable abilities of the proposed method. The results are compared with several recently proposed state-of-the-art algorithms and the simulation results show that the proposed method outperforms other algorithms. Further, the LSMA-TLBO is executed through statistical testing of hypothesis namely Wilcoxon's rank-sum test. It is concluded that the proposed LSMA-TLBO is more competitive and effective algorithm to solve real-world complex OPs.

关键词： Slime mould algorithm teaching learning based optimization L?vy flight Hybrid algorithms IEEE CEC-C06 2019 Engineering design problems

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An approach for extractive text summarization using fuzzy evolutionary and clustering algorithms

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： Verma, Pradeepika Verma, Anshul Pal, Sukomal IIT BHU Varanasi Dept Comp Sci & Engn Varanasi Uttar Pradesh India BHU Dept Comp Sci Varanasi Uttar Pradesh India

Automatic text summarization schemes are indeed helpful for glancing briefly at the text document. With this motivation, we introduce here a two-stage hybrid model for text summarization task by utilizing the strength of various approaches. In the first step, we cluster the sentences of a document according to their similarity using a partitional clustering algorithm. We then use a linear combination of the normalized Google distance and word mover's distance to differentiate two sentences. The notion of gap statistics is exploited to approximate the number of partitions for the given document needed in the partitional clustering algorithm. We extract the significant sentences from each cluster (partition), which are recognized by their adjusted text feature scores, in the second step. The teaching-learning based optimization approach is used to find the optimal weights for the text features whereas a fuzzy inference system with a full-fledged knowledge base generated by humans is employed to determine the final score of the sentences. Moreover, we have also proposed an exact method to give a solution for the summarization problem by modeling it as an Integer Linear Programming (ILP) problem. We evaluate the proposed methods on three different datasets: DUC 2001, DUC 2002, and CNN. The observed results on these standard datasets manifest the efficacy of the proposed methods. We further show that partitioning a document in an optimal number of clusters plays a major role in content coverage in summaries. The performance of the proposed hybrid method shows that the combination of fuzzy, evolutionary, and clustering algorithms produces good summaries of the documents. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Document summarization teaching learning based optimization Gap statistics Exact method Fuzzy logic

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