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An adaptive inertia weight teaching-learning-based optimization algorithm and its applications

APPLIED MATHEMATICAL MODELLING 2020年第Part1期77卷 309-326页

作者： Shukla, Alok Kumar Singh, Pradeep Vardhan, Manu NIT Raipur Dept Comp Sci & Engn Chhattisgarh India

This paper presents an effective metaheuristic algorithm called teaching learning-based optimization which is widely applied to solve the various real-world optimization problems. However, teaching learning-based optimization is rapidly trapped into local optima. To handle this kind of problem, we proposed an improved teaching learning-based optimization algorithm using adaptive exponential distribution inertia weight and altering the position-updating equation. In addition, the logistic map is applied to generate a uniformly distributed population to enhance the quality of the initial populations, The performance of the proposed method is evaluated on a suite of benchmark functions with different characteristics. The efficiency of the proposed technique is also evaluated on six gene expression datasets with the help of three classifiers. The experimental result demonstrates that the proposed method is comparatively useful in adapting the inertia weight in comparison to the existing inertia weight strategies with regards to the quality of solutions, convergence rate along with classification accuracy. In particular gene selection, the proposed method has achieved up to 98% classification accuracy for three out of six datasets with optimal gene subsets for all six datasets and maximum accuracy is achieved as 100% in small round blue-cell tumor dataset. (C) 2019 Elsevier Inc, All rights reserved.

关键词： teaching learning-based optimization Gene selection Benchmark Classification Inertia weight Metaheuristic

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Steady state and dynamic performance of self-excited induction generator using FACTS controller and teaching learning-based optimization algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第1期37卷 77-97页

作者： Elkholy, Mahmoud M. Zagazig Univ Dept Elect Power & Machines Zagazig Egypt

Purpose - The paper aims to present an application of teaching learning-based optimization (TLBO) algorithm and static Var compensator (SVC) to improve the steady state and dynamic performance of self-excited induction generators (SEIG). Design/methodology/approach - The TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with different types of prime mover. For a constant speed prime mover, the TLBO algorithm attains the optimal capacitance to have rated load voltage at different loading conditions. In the case of variable speed prime mover, the TLBO methodology is used to obtain the optimal capacitance and prime mover speed to have rated load voltage and frequency. The SVC of fixed capacitor and controlled reactor is used to have a fine tune in capacitance value and control the reactive power. The parameters of SVC are obtained using the TLBO algorithm. Findings - The whole system of three-phase induction generator and SVC are established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 7.5 kW and 1.5 kW) using the TLBO algorithm and SVC. An experimental setup is built-up using a 1.5 kW three-phase induction machine to confirm the theoretical analysis. The TLBO results are matched with other meta heuristic optimization techniques. Originality/value - The paper presents an application of the meta-heuristic algorithms and SVC to analysis the steady state and dynamic performance of SEIG with optimal performance.

关键词： Self-excited induction generators Static VAR compensator teaching learning-based optimization

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A chaotic teaching learning based optimization algorithm for clustering problems

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APPLIED INTELLIGENCE 2019年第3期49卷 1036-1062页

作者： Kumar, Yugal Singh, Pradeep Kumar JUIT Dept Comp Sci & Engn Waknaghat Himachal Pardes India

This paper presents a teaching learning based algorithm for solving optimization problems. This algorithm is inspired through classroom teaching pattern either students can learn from teachers or from other students. But, the teaching learning based optimization (TLBO) algorithm suffers with premature convergence and lack of tradeoff between local search and global search. Hence, to address the above mentioned shortcomings of TLBO algorithm, a chaotic version of TLBO algorithm is proposed with different chaotic mechanisms. Further, a local search method is also incorporated for effective tradeoff between local and global search and also to improve the quality of solution. The performance of proposed algorithm is evaluated on some benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC'14). The results revealed that proposed algorithm provides better and effective results to solve benchmark test functions. Moreover, the proposed algorithm is also applied to solve clustering problems. It is found that proposed algorithm gives better clustering results in comparison to other algorithms.

关键词： teaching learning-based optimization Clustering Meta-heuristics Numerical function optimization

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Fuzzy Adaptive teaching learning-based optimization Strategy for GUI Functional Test Cases Generation 2018

Fuzzy Adaptive Teaching Learning-based Optimization Strategy...

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7th International Conference on Software and Computer Applications (ICSCA)

作者： Din, Fakhrud Zamli, Kamal Z. Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia Univ Malakand Dept Comp Sci & IT Kpk Pakistan

ISBN: (纸本)9781450354141

Graphical User Interface (GUI) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with GUIs. Unlike command based interaction, GUI uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. GUI testing is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, GUI functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive teaching learning-based optimization (ATLBO) algorithm, a variant of the basic teaching learning-based optimization (TLBO) algorithm, for GUI functional testing is proposed. ATLBO utilizes Event-Interaction Graph (EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.

关键词： GUI Functional Testing teaching learning-based optimization Mamdani Fuzzy Inference System Model-based testing

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Fuzzy Adaptive teaching learning-based optimization Strategy for GUI Functional Test Cases Generation

Fuzzy Adaptive Teaching Learning-based Optimization Strategy...

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2018年第七届软件与计算机应用国际会议

作者： Fakhrud Din Kamal Z.Zamli Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Department of Computer Science & IT University of Malakand KPK

Graphical User Interface(GUI) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with GUIs. Unlike command based interaction, GUI uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. GUI testing is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, GUI functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive teaching learning-based optimization(ATLBO) algorithm, a variant of the basic teaching learning-based optimization(TLBO) algorithm, for GUI functional testing is proposed. ATLBO utilizes Event-Interaction Graph(EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.

关键词： GUI Functional Testing teaching learning-based optimization Mamdani Fuzzy Inference System Model-based testing

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Neighbour teaching learning based optimization for global optimization problems

Neighbour teaching learning based optimization for global op...

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3rd International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS)

作者： Shukla, Alok Kumar Singh, Pradeep Vardhan, Manu NIT Raipur Dept Comp Sci & Engn Chhattisgarh 492010 CG India

In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering and scientific field. One of the intelligent evolutionary meta-heuristic algorithms is teaching learning based optimization (TLBO). The basic TLBO algorithm follows the isolated learning strategy for the whole population. This invariable learning strategy may cause the misconception of knowledge for a specific learner, which makes it unable to deal with different complex situations. For solving the complex non-linear optimization problems, local optimum frequently happens in the generating process. To resolve these kinds of problem, this paper introduces Neighbour based TLBO (NTLBO) and differential mutation. The concept of neighbour learning and differential mutation is introduced to improve the convergence solution after each run of experiment. Neighbour learning method maintains the explorative and exploitation search of the population and discourages the premature convergence. The efficiency of the proposed algorithm is evaluated on eight benchmark functions of Congress on Evolutionary Computation (CEC) 2006. The proposed NTLBO present extensive comparative study with the state-of-the-art forms of the meta-heuristic algorithms for standard benchmark functions. The result shows that the proposed NTLBO gives the superior performance over recent meta-heuristic algorithms.

关键词： Differential mutation explorative and exploitation meta-heuristic neighbour learning teaching learning-based optimization

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Chaos teaching learning based algorithm for large-scale global optimization problem and its application

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第1期34卷

作者： Shukla, Alok Kumar Vellore Inst Technol VIT AP Univ Dept Comp Sci & Engn Amaravati Andhra Pradesh India

teaching learning-based optimization (TLBO) is a popular stochastic algorithm that has recently been widely applied in a variety of optimization problems since its start. In TLBO algorithm, the concept of chaos not only shows a vital effect in its convergence but also plays a substantial role to balance of exploration and exploitation through evolution. However, TLBO is quickly trapped in local optima and premature convergence seems when applied to sophisticated complex functions. To handle these problems, we introduced an improved TLBO algorithm using chaotic concept. To achieve ability to search for exploration and exploitation, new phase called chaotic phase is added in original TLBO algorithm. The proposed method is thoroughly evaluated on benchmark test suites. The numerical result show that proposed method is relatively effective in adapting the chaotic value regarding original TLBO in terms of solution quality and convergence rate. In addition, performance of proposed method is evaluated on benchmark KDD Cup 99 intrusion dataset. The experimental results demonstrate that proposed method achieves higher predictive accuracy, detection rate, false alarm rate, and provided more significant features as compared with other wrapper techniques.

关键词： accuracy chaotic KDD cup 99 optimization teaching learning-based optimization

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optimization of wind farm power output using wake redirection control

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RENEWABLE ENERGY 2024年 235卷

作者： Balakrishnan, Raj Kiran Son, Eunkuk Hur, Sung-ho Kyungpook Natl Univ Sch Elect & Elect Engn 80 Daehakro Daegu 41566 South Korea Korea Inst Energy Res Jeju Global Res Ctr JGRC Wind Energy Res Dept 200 Haemajihaean Ro Jeju Si 63357 Jeju Do South Korea

The wake effect, which is caused by the upstream turbines in a wind farm, adversely affects the efficiency of downstream turbines, leading to reduced energy generation and increased turbine fatigue loading. To mitigate this effect, a real-time wind farm control technique, i.e., wake redirection control (WRC), employing teaching learning-based optimization (TLBO) is introduced. This technique redirects the wakes away from the downstream turbines in real time, allowing them to generate more power by sacrificing some of the power generated by the upstream turbines. As a result, the total power generated by the wind farm is maximized. A low-fidelity 20-turbine real-life offshore wind farm is modeled and simulated in FLORISSE_M, the MATLAB version of the FLORIS (FLOw Redirection and Induction in Steady-state). The power produced by the wind farm model is maximized in real time by employing TLBO. The optimization results (i.e., the optimized yaw angles) are validated using the corresponding high-fidelity wind farm model developed in SOWFA (Simulator fOr Wind Farm Applications). Various results are presented to demonstrate that the TLBO-based WRC positively affects the power generated by the wind farm.

关键词： optimization teaching learning-based optimization Wake effect Wake redirection control Wind farm control

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Characterisation of basalt/glass/kevlar-29 hybrid fibre-reinforced plastic composite material through Nd: YAG laser drilling and optimisation using stochastic methods

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第8期38卷 4321-4331页

作者： Sahoo, Amiya Kumar Mishra, Dhananjay R. Jaypee Univ Engn & Technol Dept Mech Engn AB Rd Guna 473226 Madhya Pradesh India Jaypee Univ Engn & Technol Ctr Excellence Renewable Energy Dept Mech Engn A-B Rd Guna 473226 Madhya Pradesh India

A newly developed and hand layup hybrid FRP of Basalt, Glass and Kevlar-29 was considered for the characterisation of Nd: YAG laser drilling and optimisation using stochastic methods. Laser current (LC), pulse width (PW), stand-off distance (SoD), and compressed air pressure (CAP) were the laser parameters. The Box-Behnken designed laser drilling experiments were completed to characterise through hole taper angle and average overcut. The grey relational grade (GRG) of the responses was considered for optimisation using a genetic algorithm (GA), teaching learning-based optimisation (TLBO) and golden jackal optimisation (GJO). The predicted optimum setting was LC of 180 A, SoD of 1 mm and CAP of 8 kg/cm2. The GA & TLBO and GJO predicted PW of 3 ms with GRG of 0.9948 and 2.625 ms with GRG of 0.9203, respectively. The experimental verification of the predicted conditions acknowledges the predicted values of the responses with acceptable errors.

关键词： Fibre-reinforced plastic Hybrid composite Laser drilling Stochastic methods Genetic algorithm teaching learning-based optimization Golden jackal optimisation

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Intelligent computation modeling and analysis of a gripper for advanced manufacturing application

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第5期17卷 2185-2195页

作者： Duc Nam Nguyen Minh Phung Dang Tan Thang Nguyen Thanh-Phong Dao Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Ind & Trade Coll Fac Mech Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

This paper is aimed to provide a new design modeling of the gripper via using the intelligent computing model. The gripper is designed to get benefit of a symmetric structure and compliant mechanism that can manipulate the objects with a stable force. The numerically experimental samples for the gripper are built and the finite element simulations are implemented. The displacement of left hand is collected. An intelligent computing model is formulated via a hybridization of the teaching learning optimization and feed forward neural network. The teaching learning optimization algorithm is embedded into neural network to enhance the training process. The results determined that the mean square error values of the entire model, the training, the testing, and validating are about 6.04e-07, 6.11e-07, 6.50e-08, and 1.10e-06, respectively. Furthermore, the coefficient of determination value of the entire model, the training, the testing, and validating are 0.9975, 0.9970, 0.9998, and 0.9677, accordingly. In addition, the proposed intelligent predictor is outperformed other regression methods such as linear regression, full 2nd order polynomial regression, and traditional artificial neural network. Moreover, the errors among the estimated from the proposed intelligent method and the prediction errors are less than 3%. It revealed that the proposed intelligent methodology is a well-suitable predictor for modeling the behaviors of gripper. The gripper is capable of providing a displacement amplification ratio of 2.85 and a max grasping force of 145.96 N. The gripper is potential for many practical applications such as robotics and manipulators in agricultural and electrics engineering.

关键词： Intelligent method Gripper Compliant mechanism Artificial neural network teaching learning-based optimization

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