Quadratic interpolation based teaching-learning-based optimization for chemical dynamic system optimization

Xu, Chunxiao; Mei, Congli; Xu, Bin; Yu, Kunjie; Huang, Xiuhui

doi:10.1016/j.knosys.2018.01.021

Cited by 65 publications

(33 citation statements)

References 63 publications

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“…TLBO's greatest advantage is its engaging characters, such as easy to execute and fast convergence. It has received strong attention and has been expanded to deal with constrained objective, large-scale, and dynamic optimization problems [19]. Several engineering problems, such as neural network training, power grid dispatch, and output scheduling have been discussed using TLBO variants [20][21][22].…”

Section: Learning Enthusiasm Based Tlbo (Lebtlbo)mentioning

confidence: 99%

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

2020

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Section: Learning Enthusiasm Based Tlbo (Lebtlbo)mentioning

confidence: 99%

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

2020

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“…For the flow missing data from the hydrology station, the interpolation methods can be used because of its continuity. The missing flow data is filled by quadratic interpolation [22] in this paper.…”

Section: A Study Area and Data Processmentioning

confidence: 99%

CRML: A Convolution Regression Model With Machine Learning for Hydrology Forecasting

et al. 2019

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Hydrologic disasters often result in substantial property damage and casualties. Therefore, hydrology forecasting, especially the flooding, has become a hot research spot in all countries of the world. Based on the basic principle of flooding formation, this paper proposes a data-driven hydrology forecasting model, i.e., the CRML (Convolution Regression based on Machine Learning). This model could reflect the impact of hourly rainfall on the future flow changes and the flow changes are predicted by superimposing these impacts. First, our work is implemented on historical data onto the Xixian River Basin in Henan Province, China. Through the data filtering, the training set of our model is constructed by using the flood process selection algorithm proposed in this paper. Next, the gradient descent algorithm is used to update the weights of the model, and the optimal weights are verified by ten flooding events generated in the past ten years. Finally, the numerical results show that the qualified rates of our model in predicting flood peak flow and its arrival time is approximately 90% and 100%, respectively. Compared with the latest popular artificial intelligence schemes, our model structure is clear and concise. And combined with the physical meaning of the traditional model and machine learning technology, our model can accurately complete the task of long and short lead time hydrology forecasting.

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“…(1); (11) Evaluate the new learner x ,new ; (12) Accept x ,new if it is better than the old one x ,old (13) // Learner phase // (14) Randomly select another learner x which is different from x ; (15) Update the learner according to Eq. (2); (16) Evaluate the new learner x ,new ; (17) Accept x ,new if it is better than the old one x ,old ; (18) end for (19) The second category is the hybrid TLBO method by combining it with other search strategies. Ouyang et al [27] incorporated a global crossover (GC) operator to balance the local and global searching and presented a new version of TLBO called TLBO-GC.…”

Section: Improvements On Tlbomentioning

confidence: 99%

“…TLBO utilizes two productive operators, namely, teacher phase and learning phase to search good solutions [12]. Due to its attractive characters such as simple concept, without the specific algorithm parameters, easy implementation, and rapid convergence, TLBO has captured great attention and has been extended to handle constrained [13], multiobjective [14], large-scale [15], and dynamic optimization problems [16]. Furthermore, TLBO has also been successfully applied to many scientific and engineering fields, such as neural network training [17], power system dispatch [18], and production scheduling [19].…”

Section: Introductionmentioning

confidence: 99%

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

et al. 2018

Journal of Applied Mathematics

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

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Quadratic interpolation based teaching-learning-based optimization for chemical dynamic system optimization

Cited by 65 publications

References 63 publications

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

CRML: A Convolution Regression Model With Machine Learning for Hydrology Forecasting

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

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