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Prediction of quality using ANN based on teaching-learning optimization in component-based software systems

SOFTWARE-PRACTICE & EXPERIENCE 2018年第4期48卷 896-910页

作者： Tomar, Pradeep Mishra, Rajesh Sheoran, Kavita Gautam Buddha Univ Sch Informat & Commun Technol Greater Noida 201308 India Gautam Buddha Univ Dept Elect & Commun Engn Greater Noida 201308 India Gautam Buddha Univ Dept Comp Sci & Engn Greater Noida 201308 India

The primary objective of our research work is to enhance the prediction of the quality of a component-based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN-supported teaching-learning optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the teaching-learning-based optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall-out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.

关键词： artificial neural network bounded interface complexity metric interface surface consistency self-completeness of component's parameter self-completeness of component's return value teaching-learning-based optimization

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Multi-objective parametric optimization on machining of Inconel-825 using wire electrical discharge machining

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第20期234卷 4056-4068页

作者： Shandilya, Pragya Rouniyar, Arun Kumar Saikiran, D. Motilal Nehru Natl Inst Technol Dept Mech Engn Allahabad 211004 Uttar Pradesh India

Wire electrical discharge machining is a thermal energy-based non-conventional machining process which can machine conductive materials with high precision. In this present work, machining of Inconel-825 was performed using wire electrical discharge machining. Multi-objective parametric optimization was performed for maximum cutting rate and minimum surface roughness using teaching-learning-based optimization, grey relational analysis, and genetic algorithm. Four wire electrical discharge machining parameters, namely spark off time (S-OFF), spark on time (S-ON), peak current (I-P), and angle of cutting (A) were considered. Comparison of optimum wire electrical discharge machining parameters through teaching-learning-based optimization, grey relational analysis, and genetic algorithm was performed. The better optimum solution for wire electrical discharge machining parameters was obtained using teaching-learning-based optimization and optimum values were at I-P (1 A), S-ON (30 mu s), S-OFF (12.5 mu s), and A (44.8 degrees) with cutting rate as 19.744 mm/min and surface roughness as 1.331 mu m. The optimum results obtained using optimization techniques were validated with the experimental results and error was observed to be within 5%. Moreover, response surface models were developed to predict the cutting rate and surface roughness in terms of wire electrical discharge machining parameters using analysis of variance.

关键词： Inconel-825 process parameters surface roughness cutting rate teaching-learning-based optimization wire electrical discharge machining

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A neutrosophic set-based TLBO algorithm for the flexible job-shop scheduling problem with routing flexibility and uncertain processing times

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 2833-2853页

作者： Jin, Liangliang Zhang, Chaoyong Wen, Xiaoyu Sun, Chengda Fei, Xinjiang Shaoxing Univ Dept Mech Engn Shaoxing Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China Zhengzhou Univ Light Ind Henan Key Lab Intelligent Mfg Mech Equipment Zhengzhou Peoples R China

Different with the plain flexible job-shop scheduling problem (FJSP), the FJSP with routing flexibility is more complex and it can be deemed as the integrated process planning and (job shop) scheduling (IPPS) problem, where the process planning and the job shop scheduling two important functions are considered as a whole and optimized simultaneously to utilize the flexibility in a flexible manufacturing system. Although, many novel meta-heuristics have been introduced to address this problem and corresponding fruitful results have been observed;the dilemma in real-life applications of resultant scheduling schemes stems from the uncertainty or the nondeterminacy in processing times, since the uncertainty in processing times will disturb the predefined scheduling scheme by influencing unfinished operations. As a result, the performance of the manufacturing system will also be deteriorated. Nevertheless, research on such issue has seldom been considered before. This research focuses on the modeling and optimization method of the IPPS problem with uncertain processing times. The neutrosophic set is first introduced to model uncertain processing times. Due to the complexity in the math model, we developed an improved teaching-learning-based optimization(TLBO) algorithm to capture more robust scheduling schemes. In the proposed optimization method, the score values of the uncertain completion times on each machine are compared and optimized to obtain the most promising solution. Distinct levels of fluctuations or uncertainties on processing times are defined in testing the well-known Kim's benchmark instances. The performance of computational results is analyzed and competitive solutions with smaller score values are obtained. Computational results show that more robust scheduling schemes with corresponding neutrosophic Gantt charts can be obtained;in general, the results of the improved TLBO algorithm suggested in this research are better than those of other algorithms w

关键词： Neutrosophic sets Flexible job-shop scheduling Processing planning teaching-learning-based optimization TLBO Integrated process planning and scheduling IPPS Uncertain processing times

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Enhancing Education and teaching Management Through Data Mining and Support Vector Machine Algorithm

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INTERNATIONAL JOURNAL OF E-COLLABORATION 2024年第1期20卷 1页

作者： Zhang, Peipeng Zhang, Fangfang Jiyuan Vocat & Tech Coll Jiyuan Peoples R China Hebei Vocat Univ Ind & Technol Shijiazhuang Peoples R China

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with teaching-learning-based optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

关键词： Educational Data Mining learning Ability Evaluation Machine learning Predictive Accuracy Support Vector Machines teaching-learning-based optimization

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Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method

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ENERGIES 2023年第4期16卷 1781页

作者： Krishnamurthy, Nandini K. Sabhahit, Jayalakshmi N. Jadoun, Vinay Kumar Gaonkar, Dattatraya Narayan Shrivastava, Ashish Rao, Vidya S. Kudva, Ganesh Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India Natl Inst Technol Karnataka Dept Elect & Elect Engn Mangalore 575025 India Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Gurugram 122003 Haryana India Manipal Acad Higher Educ Manipal Inst Technol Dept Instrumentat & Control Engn Manipal 576104 Karnataka India

In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network's operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 x 10(6) for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20;16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs' optimal placement at bus numbers 2, 18, 30;60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

关键词： DC microgrid electric vehicle charging station fuel cell renewable energy sources teaching-learning-based optimization

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TLBO merged with studying effect for Economic Environmental Energy Management in High Voltage AC Networks Hybridized with Multi-Terminal DC Lines

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Sarhan, Shahenda El-Sehiemy, Ragab A. Shaheen, Abdullah M. Gafar, Mona King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafr Al Sheikh Egypt Suez Univ Fac Engn Elect Engn Dept Suez Egypt Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Sulail Dept Comp Sci Kharj 16278 Saudi Arabia Kafrelsheikh Univ Machine Learning & Informat Retrieval Dept Artificial Intelligence Kafrelsheikh 33516 Egypt

Nowadays, different types of non-linear and multi-modal hybridized alternating current (AC) power grids with multi-terminal high voltage direct current (HVDC) are of great importance. Energy management aims to reduce costs, network power losses, and environmental pollutants in AC-HVDC electricity grids. This paper describes an improved variant of TLBO (teaching-learning-based optimization) entitled teaching-learning studying-based optimizer (TLSBO) that improves TLBO's global optimization performance. Nevertheless, an advanced model of preventive strategy action is presented that offers an appropriate margin in power output and power transfer through transmission lines to plan for possible contingencies. Following that, a contingency study is conducted to guarantee the system's operation. Also, a corrective strategy action via Re-performing the energy management in the high voltage AC-DC network considering the most critical lines that prevents the system from converging. The suggested upgrade is formed by the inclusion of a change initiative to TLBO known as the studying approach, wherein one individual borrows additional data from some other randomized member in improving its situation. The suggested TLSBO is assessed and contrasted to TLBO and various existing approaches on modified IEEE 30-bus, 57-bus, and large-scale 118-bus AC-HVDC systems. Also, the efficiency of the proposed TLSBO is declared versus the top three techniques in CEC2020 Competition. Compared to the standard TLBO, the proposed TLSBO achieves improvements of 0.04, 4.25, 15.65 and 65.84% for the best, mean, worst and standard deviation metrics, respectively. The simulation outcomes show that the developed TLSBO outperforms the others in terms of efficacy and resilience. TLSBO, on the other hand, has convergence speed and superior quality for the ultimate optimal solution, as well as greater power for escape from converging to local optima than basic TLBO.& COPY;2023 Elsevier B.V. All rights reser

关键词： teaching-learning-based optimization Studying-based approach HVDC lines Economic emission power flow Valve point impacts

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An Improved Harmony Search based on teaching-learning Strategy for Unconstrained Binary Quadratic Programming 33

An Improved Harmony Search Based on Teaching-Learning Strate...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Yong, Longquan Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China Shaanxi Key Lab Ind Automat Hanzhong 723001 Peoples R China

ISBN: (纸本)9781665440899

Unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm based on Harmony Search (HS) and teaching-learning-based optimization. The main features of the proposed algorithm called harmony search with teaching-learning (HSTL) are the integration of teaching-learning strategy in the basic harmony search. This hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the teaching-learning-based optimization. Experiments on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.

关键词： Unconstrained binary quadratic programming Harmony search teaching-learning-based optimization

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A metaheuristic-based optimum tuning approach for tuned liquid dampers for structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第3期31卷 e1907-e1907页

作者： Ocak, Ayla Bekdas, Gebrail Nigdeli, Sinan Melih Istanbul Univ Cerrahpasa Dept Civil Engn Istanbul Turkey

This study focuses on the optimization of tuned liquid dampers (TLDs) by using metaheuristic methods using a natural phenomenon as inspiration. Unlike tuned mass dampers (TMDs), TLDs consist of two parts of bodies that contain the passive liquid that is stationary and the active part of the liquid that involves in sloshing. To optimize the amount of optimum active and passive liquids by finding the best dimensional properties of the liquid tank, a two-degree-of-freedom (DOF) model was used as TLD in the dynamic analysis used in the optimization that aims to reduce the maximum displacement of the structure under earthquake excitations. For this purpose, water, oil, and glycerin liquids were selected for TLD. TLD parameters were optimized with the help of Jaya algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, and teaching-learning-based optimization (TLBO). Total acceleration and maximum displacement were evaluated after optimization. The performance of TLD under earthquake excitations has been also compared with TMDs. According to the results, the optimized TLD has a better performance than TMD, and Jaya algorithm has a slightly better performance than the other algorithms.

关键词： flower pollination algorithm harmony search algorithm Jaya algorithm optimization teaching-learning-based optimization tuned liquid damper (TLD)

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Inertia-weight local-search-based TLBO algorithm for energy management in isolated micro-grids with renewable resources

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷 107877-107877页

作者： Abaeifar, Amin Barati, Hassan Tavakoli, Ali Reza Islamic Azad Univ Dept Elect Engn Dezful Branch Dezful Iran Islamic Azad Univ Dept Elect Engn Ahvaz Branch Ahvaz Iran

Integration of distributed generators and loads in the form of a microgrid enhances the supply reliability and reduces the generation costs and greenhouse gas emissions. Microgrids should be properly managed via an energy management system (EMS) to minimize the overall costs of the system. In this paper, an Inertia-Weight Local-Search based teaching-learning-based optimization (named IWLS-TLBO), is proposed as an exploration-exploitation balanced metaheuristic algorithm to solve the EMS problem. The proposed IWLS-TLBO algorithm is inspired by the principles of human learning based on self-perceptual and self-regulatory abilities of people in facing various problems and adopting the best decision based on the previous experiences. By emphasizing of the IWLS-TLBO algorithm on both exploration (ability to search for new solutions) and exploitation (ability to exploit from existing solutions), the search space can be more effectively investigated than the original TLBO algorithm. In order to justify the performance of the proposed IWLS-TLBO algorithm, obtained results on different bench-mark functions are compared with those of achieved by the original TLBO and other metaheuristics. Then, the IWLS-TLBO is applied to optimize the EMS problem which is formulated based on a unit commitment problem in an isolated microgrid with renewable energy resources. Simulation results demonstrate the superiority of the proposed IWLS-TLBO algorithm against the existing metaheuristic algorithms, in terms of solution quality and convergence speed.

关键词： Microgrids Renewable energy resources Energy management system Unit commitment teaching-learning-based optimization

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A note on teaching-learning-based optimization algorithm

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INFORMATION SCIENCES 2012年 212卷 79-93页

作者： Crepinsek, Matej Liu, Shih-Hsi Mernik, Luka Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA CALTECH Pasadena CA 91125 USA

teaching-learning-based optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO's dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step;(2) incorrect formulae on a number of fitness function evaluations;and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics' researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Constrained optimization problems Unconstrained optimization problems Experimental replication

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