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 Teachi...
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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.
Unconstrained binary quadratic programming(UBQP) problem plays an important role in operational research due to its application potential and its computational *** paper presents a new hybrid algorithm based on Harmon...
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Unconstrained binary quadratic programming(UBQP) problem plays an important role in operational research due to its application potential and its computational *** paper presents a new hybrid algorithm based on Harmony Search(HS) and teaching-learning-based *** main features of the proposed algorithm called harmony search with teaching-learning(HSTL) are the integration of teaching-learning strategy in the basic harmony *** hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the teaching-Leaming-based *** on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.
based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metabasedoptimization (TLBO), Differen...
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based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metabasedoptimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in finetuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-basedlearning Henry Gas Solubility optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.
This paper proposes a Hybrid Cuckoo search algorithm to solve Multi-area economic dispatch problem ( MAED). Hybrid Cuckoo search algorithm is a combination of the Cuckoo search algorithm and teaching-learning-based op...
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ISBN:
(纸本)9781467396820
This paper proposes a Hybrid Cuckoo search algorithm to solve Multi-area economic dispatch problem ( MAED). Hybrid Cuckoo search algorithm is a combination of the Cuckoo search algorithm and teaching-learning-based optimization, where the learner phase of TLBO is added to improve performance of Cuckoo eggs. The proposed method has been applied for solving three tested cases of Multi-area economic dispatch problem. The objective of this problem is to minimize a total generation cost while satisfying generator operational constraints and tie-line constraints. The proposed method has been compared with the conventional Cuckoo search algorithm and teaching-learning-based optimization to obtain its effectiveness. Numerical results show that the proposed method gives better solutions than two compared methods with high performance.
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