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An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method

Journal of Industrial Engineering International 2019年第1期15卷 181-192页

作者： Buddala, Raviteja Mahapatra, Siba Sankar Department of Mechanical Engineering National Institute of Technology Rourkela Rourkela 769008 Odisha India

In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. teaching–learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’s instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP. © 2018, The Author(s).

关键词： Flexible job shop scheduling Local search Makespan Meta-heuristics teaching–learning-based optimization

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Parameter estimation of an SRGM using teaching learning based optimization

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International Journal of Information Technology (Singapore) 2023年第6期15卷 2941-2950页

作者： Dhaka, Ramgopal Pachauri, Bhoopendra Jain, Anamika Department of Mathematics and Statistics Manipal University Jaipur Jaipur 303007 India

A testing-effort function is an important factor for software reliability enhancement. It describes the distribution of an effort and its effectiveness throughout the exposure. Some distribution functions like log-logistic, exponential, Weibull, modified Weibull, etc. have already been used as a testing-effort function but these are limited due to their inflexibility towards examined behavior of the testing-effort function. Exponentiated additive Weibull distribution includes a set of a few well-known distributions i.e., exponentiated exponential, modified Weibull, and additive Weibull, etc. Therefore, in this manuscript, a software reliability growth model using exponentiated additive Weibull distribution as a testing-effort function has been proposed in imperfect debugging environment. Since the motive of this study is to get the best results, teaching learning based optimization and non-linear least square have been used to estimate the parameters. To validate the model, a comparative study with the existing models has been done which shows the better performance of the proposed model. Further, to calculate the optimal release time of software an optimal release policy has been discussed based on the proposed model. The proposed work may be extended by considering uncertainty in the environmental factor in the future. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Parameter estimation Software reliability growth model teaching–learning-based optimization Testing Testing effort function

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Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

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Journal of Industrial Engineering International 2018年第3期14卷 555-570页

作者： Buddala, Raviteja Mahapatra, Siba Sankar Department of Mechanical Engineering National Institute of Technology Rourkela 769008 Odisha India

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching–learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP. © 2017

关键词： Flexible flow shop JAYA algorithm Makespan Meta-heuristics teaching–learning-based optimization

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An Improved teaching-learning-based optimization With Adaptive Moment Estimation for Global optimization 23

An Improved Teaching-Learning-Based Optimization With Adapti...

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Proceedings of the 7th International Conference on Algorithms, Computing and Systems

作者： Xiaokui Wu Zhurong Zhou Duowan Wu College of Computer & Information Science Southwest University China Department of Information Service Industry Guangzhou Industry & Trade Technician College China

ISBN: (纸本)9798400709098

The teaching and learning optimization Algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, the traditional teaching process is a relatively complex process, and the simulation process of this algorithm is relatively simple. Therefore, this study introduces two new educational concepts and educational methods, "group teaching" and "adaptive learning", into the simulation process of traditional classroom teaching, and proposed an improved teaching-based optimization algorithm (TLBO-Adam) based on adaptive moment estimation. This study introduces group teaching in the "teaching stage" and uses Fuzzy C-means clustering (FCM) algorithm in the field of machine learning, and introduces Adaptive learning in the "learning stage" and uses an adaptive moment estimation algorithm with the characteristics of a goal-oriented mechanism to solve the problem of premature convergence and slow convergence of the TLBO algorithm. Finally, through designing comparative experiments, experimental results show that the improved algorithm TLBO-Adam is effective in 9 test functions and can find the global optimal solution quickly and efficiently compared with other swarm intelligence algorithms.

关键词： Adaptive Moment Estimation Fuzzy c-means algorithm Globally Optimal Solution teaching–learning-based optimization

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Process Parameters optimization for Inconel-825 in WEDM Using TLBO Algorithm 1st

Process Parameters Optimization for Inconel-825 in WEDM Usin...

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1st International and 4th National Conference on Reliability and Safety Engineering, INCRS 2018

作者： Saikiran, D. Rouniyar, Arun Kumar Shandilya, Pragya Department of Mechanical Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Allahabad211004 India

ISBN: (纸本)9789811537455

To achieve best manufacturing conditions, we have to manufacture quality products at minimum cost and minimum manufacturing lead time, and this can be done with optimization. The effective optimization of machining process parameters dramatically affects the cost and production time of machined components as well as the quality of the final products. Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts of hard materials with complex shapes. Owing to its complex process, selection of optimal parameter values enhances the performance of the WEDM. The present work deals with single optimization in minimizing the surface roughness of Inconel-825 alloy, in WEDM process using teaching–learning-based optimization (TLBO) algorithm. Three input process parameters of Wire EDM (i.e. pulse on time, pulse off time and peak current) are used to study their effects on the surface roughness. The optimal solution of process parameters is obtained by using TLBO approach. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Inconel Process parameters Surface roughness teaching–learning-based optimization WEDM

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An outliers detection and elimination framework in classification task of data mining

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Decision Analytics Journal 2023年 6卷

作者： Dash, Ch. Sanjeev Kumar Behera, Ajit Kumar Dehuri, Satchidananda Ghosh, Ashish Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar 751024 India Department of Computer Science Fakir Mohan University Odisha Balasore 756019 India Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

An outlier is a datum that is far from other data points in which it occurs. It can have a considerable impact on the output. Therefore, removing or resolving it before the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical results. The goal of discovering outliers in data mining is to find a pattern in data that does not conform to expected behavior. In this paper, we have proposed a framework in which a popular statistical approach termed Inter-Quartile Range (IQR) is used to detect outliers in data and deal with them by Winsorizing method. A radial basis function network trained by teaching a learning-based optimization model is developed using the preprocessed dataset under this framework. A few standard University of California Irvine (UCI)datasets are employed to measure the framework's effectiveness. The outcome of the experiments shows that our proposed framework can be a viable alternative tool for the classification task of data mining where prior outliers preprocessing is necessary. © 2023 The Author(s)

关键词： Classification Outlier detection Principal component analysis Radial basis function neural networks teaching–learning-based optimization Winsorizing

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