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Multiobjective Phase-Wise teaching learning-based optimization With No-Wait Time Heuristic for Job Shop Scheduling Problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Kommadath, Remya Maharana, Debasis Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati Assam India Indian Inst Technol Guwahati Sch Business Gauhati Assam India

This study proposes a multiobjective variant of the phase-wise teaching learning-based optimization algorithm, namely Non-dominated Sorting phase-wise teaching learning-based optimization (NSpTLBO), for solving the multiobjective job shop scheduling problems with unrelated parallel machines. The proposed technique is integrated with a no-wait time heuristic mechanism that reschedules the jobs assigned to the machines so as to minimize the constraint violation. Such an approach is implemented to facilitate the determination of feasible solutions in the earlier iterations of the metaheuristic technique. The efficacy of the proposed multiobjective technique is tested on job shop scheduling problems having jobs with release and due time. The performance of the proposed NSpTLBO and the hybrid NSpTLBO heuristic mechanism is compared against five multiobjective metaheuristic and hybrid metaheuristic-heuristic techniques. based on the hypervolume and coverage metric, the performance of NSpTLBO with the heuristic mechanism is observed to be competitive compared with other algorithms as well as with its stand-alone version. This study has found the presence of the heuristic mechanism to be beneficial to all the multiobjective metaheuristic techniques in determining better-converged and diversified solutions against their stand-alone algorithms as hybrid techniques have provided a better hypervolume ratio than their stand-alone counterpart.

关键词： heuristic mechanism metaheuristic techniques multiobjective optimization scheduling teaching learning-based optimization

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A multi-objective QoS-aware IoT service placement mechanism using teaching learning-based optimization in the fog computing environment

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NEURAL COMPUTING & APPLICATIONS 2024年第7期36卷 3415-3432页

作者： Sha, Yan Wang, Hui Wang, Dan Ghobaei-Arani, Mostafa Xuzhou Med Univ Sch Med Informat & Engn Xuzhou 221004 Jiangsu Peoples R China Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran Islamic Azad Univ Prod & Recycling Mat & Energy Res Ctr Qom Branch Qom Iran

The huge amount and diversity of data generated by Internet of Things (IoT) devices and the need to store and process this data led to the development of fog computing alongside cloud computing. Fog computing is a new paradigm for providing service at the edge of the network and close to end users, so that it can support real-time IoT applications. Because fog involves heterogeneous and distributed infrastructure with limited resources, so efficient resource allocation to satisfy Quality of Service (QoS) is challenging. IoT application placement mechanisms have been developed to address these issues, in which the subordinate services of these applications are mapped to fog nodes. Despite extensive research to solve the Service Placement Problem (SPP) in fog computing, efforts are still ongoing due to the importance of the issue. Hence, this paper proposes an efficient and autonomous mechanism for solving SPP using teaching learning-based optimization (TLBO) called SPP-TLBO. SPP-TLBO is a multi-objective QoS-aware algorithm that manages resources on distributed and localized fog domains. In addition to the above, we improve the performance of TLBO by configuring the evolution process with a shared parallel architecture. Besides, SPP-TLBO can save more resources to handle future requests by considering application deadlines and extracting the dynamic distribution of resources required over time. The proposed algorithm is evaluated by simulation on a synthetic fog environment. The simulation results show that SPP-TLBO improves system performance and is between 8 and 19% better efficiency compared to some advanced methods such as CSA-FSPP, FSP-ODMA and, WOA-FSP.

关键词： Fog computing environment IoT service placement Autonomous mechanism teaching learning-based optimization

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Efficient Computation Offloading of IoT-based Workflows Using Discrete teaching learning-based optimization

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Computers, Materials & Continua 2022年第11期73卷 3685-3703页

作者： Mohamed K.Hussein Mohamed H.Mousa Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt Department of Information Technology College of Computer Science at AlKamilUniversity of JeddahJeddahSaudi Arabia

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent *** exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and *** this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC *** proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge *** addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)*** experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

关键词： High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization

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Distributed generator and capacitor-embedded reconfiguration in three-phase unbalanced distribution systems using teaching learning-based optimization

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第1期32卷 e22689-e22689页

作者： Mehroliya, Shweta Arya, Anoop Maulana Azad Natl Inst Technol Bhopal Madhya Pradesh India

In the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power-transmitting media, the system must also control reactive power flow. In a three-phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning-based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33-bus standard system and a large 123-bus unbalanced system. The analysis of the IEEE 33- and 123-bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33-bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123-bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.

关键词： distributed generator multiobjectives power losses teaching learning-based optimization three-phase unbalanced system voltage stability

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Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Ullah, Wasif Ab Rashid, Mohd Fadzil Faisae Mutasim, Muhammad Ammar Nik Univ Malaysia Pahang Al Sultan Abdullah Fac Mfg & Mechatron Engn Technol Pekan 26600 Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Mech & Automot Engn Technol Pekan 26600 Malaysia

Production scheduling is a strategic process that organizes the execution of jobs on available resources to optimize specific objectives. One significant scheduling challenge is the Cost-based Hybrid Flow Shop (CHFS) problem, which involves optimizing job scheduling across multiple stages to minimize scheduling-related costs. However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted teaching-learning-based optimization (GTLBO) algorithm for CHFS. Unlike previous studies that focus on isolated cost factors, this research formulated an integrated mathematical model for CHF holistically capturing labor, energy consumption, maintenance, and late penalty costs. The GTLBO algorithm incorporates a unique hybrid initialization strategy, generating 10 % of the initial population using a Greedy algorithm to enhance exploration efficiency. The performance of GTLBO was evaluated through computational experiments involving 12 test instances, with comparative algorithms included for analysis. Results from the Wilcoxon rank-sum test indicated a significant difference between the outputs of GTLBO and other algorithms, with GTLBO outperforming the comparative algorithms in 75 % of the test instances. Additionally, the case study validation showed that GTLBO can reduce costs by 0.23 % to 4.31 % compared to other algorithms. This research offers valuable insights for manufacturers seeking to optimize CHFS scheduling to reduce production expenses.

关键词： Cost optimization Hybrid flow shop scheduling teaching learning-based optimization algorithm

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Detecting impersonation episode using teaching learning-based optimization and support vector machine techniques

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EXPERT SYSTEMS 2023年第7期40卷 e13290-e13290页

作者： Shukla, Alok Kumar Thapar Inst Engn & Technol Patiala India Thapar Inst Engn & Technol Patiala Punjab India

Over the last few decades, computer and internet security has become a vital area because of eye-opening numbers of data breaches, intruders on perilous infrastructure and malware attacks that are increasing day by day. In order to monitor abnormal activities and identify unusual attacks, several security solutions have been proposed in the recent past years. To protect computer system, intrusion detection system (IDS) opens up great opportunities to determine vulnerabilities and detect anomalies in security system. For detecting new attacks or building security solutions, in this study we present a new wrapper technique for security specialists that can help to detect more complicated attacks by combining teaching learning-based optimization with opposition learning scheme and simulated annealing method. In order to address advance attack, firstly opposition learning strategy is used to update population generation of teaching learning-based optimization that affect the robustness of the model after that simulated annealing is integrated into the teaching learning-based optimization. In proposed method to choose the relevant features, we have used support vector machine as a fitness function that can be helped to recognize attacks precisely. The proposed algorithm is evaluated on three popular datasets namely NSL-KDD, ISCX 2012 and UNSW-NB15. Experimental results show that the proposed method is superior to other existing wrapper algorithms in terms of detection rate, accuracy and false alarm rates.

关键词： intrusion detection system opposition-based learning support vector machine teaching learning-based optimization

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Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (TLBO-UEGS)

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ENERGY REPORTS 2023年 10卷 1485-1506页

作者： Sharma, Abhishek Lim, Wei Hong El-Kenawy, El-Sayed M. Tiang, Sew Sun Bhandari, Ashok Singh Alharbi, Amal H. Khafaga, Doaa Sami Grap Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 India UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Shri Guru Ram Rai Univ Sch Basic & Appl Sci Dept Math Dehra Dun 248002 India Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current-voltage relationships. However, due to the PV model's inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 x 10-04 +/- 0.0014, 1.3237 x 10-04 +/- 0.0043, and 6.6016 x 10-06 +/- 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license

关键词： Single -diode model Parameter estimation optimization teaching learning-based optimization

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A Deep learning Tool Using teaching learning-based optimization for Supporting Smart learning Environment 13th

A Deep Learning Tool Using Teaching Learning-Based Optimizat...

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13th International Conference on Blended learning (ICBL)

作者： Sooncharoen, Saisumpan Thepphakorn, Thatchai Pongcharoen, Pupong Naresuan Univ Fac Engn Ctr Operat Res & Ind Applicat CORIA Ind Engn Dept Phitsanulok 65000 Thailand Pibulsongkram Rajabhat Univ Fac Ind Technol Phitsanulok 65000 Thailand

ISBN: (纸本)9783030519681;9783030519674

Since blended learning has become one of the promising approaches to teaching and learning, the integration of traditional learning with novel innovations should be taken into account. Novel innovations can raise the level of smart learning environments as well as the construction of wisdom classrooms. There are many ways to create these innovations. One of those is to apply the deep learning tool for teaching and learning activities, that can offer the learners to have profound and thorough knowledge experience. This work proposes a novel deep learning tool, which can support teaching and learning activities especially in a topic of scheduling. A computational intelligence algorithm called teaching learning-based optimization (TLBO) was embedded within the tool for solving production scheduling problem. TLBO is a nature-inspired metaheuristic algorithm. It mimics the influential effect of teacher on learners or among learners. The tool was developed and tested upon four case studies.

关键词： Deep learning teaching learning-based optimization Scheduling Teacher phase Learner phase

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An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning

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optimization AND ENGINEERING 2019年第3期20卷 811-832页

作者： Ngoc Le Chau Minh-Quan Nguyen Thanh-Phong Dao Huang, Shyh-Chour Hsiao, Te-Ching Du Dinh-Cong Van Anh Dang Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China 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 Natl Kaohsiung Univ Sci & Technol Dept Mech Engn 415 ChienKung Rd Kaohsiung 80778 Taiwan Ton Duc Thang Univ Inst Computat Sci Div Construct Computat Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

This paper proposes an effective integration of the Taguchi method (TM), Adaptive neuro-fuzzy inference system (ANFIS) and teaching learning-based optimization (TLBO) for CNC turning optimization of S45C carbon steel. The TM plays two main roles: it reduces the number of experiments and identifies the most appropriate membership functions (MFs) and suitable learning procedure for the ANFIS. To determine the suitable ANFIS structure, we optimize the root mean squared error, a performance criterion of the ANFIS. Then, taking the established ANFIS structure, we form the virtual mathematical relations between the geometric parameters and the roughness surfaces. The results found that the triangular-shaped MFs and pi-shaped MFs are the best for the R-a and R-z roughness surfaces, respectively. The optimal parameters for ANFIS structure of R-a are found in terms of the number of input MFs of 3, the trimf MFs, hybrid learning method, and linear output MFs. The optimal parameters for ANFIS structure of R-z are determined at the number of input MFs of 3, the pimf MFs, hybrid learning method, and linear output MFs. based on the improved ANFIS establishments and optimal parameters of TLBO, the TLBO-based ANFIS is used to optimize the design parameters of the turning. We apply analysis of variance to determine the significant contribution of each factor. The results show a relative decrease in the roughness surfaces compared to those predicted by other algorithms. Therefore, the proposed optimization approach is a robust and effective tool for engineering applications.

关键词： CNC Turning Taguchi method Adaptive neuro-fuzzy inference system teaching learning-based optimization

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Sliding Mode Control Optimized by teaching learning-based optimization Algorithm for Variable Speed Wind Turbine System 5

Sliding Mode Control Optimized by Teaching Learning-Based Op...

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International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS)

作者： Dib, Faiza Boumhidi, Ismail El Akchioui, Nabil Benaya, Nabil Fac Sci El Dhar Mehraz Fes Morocco Fac Sci & Technol Al Hoceima Morocco

ISBN: (纸本)9781538678503

The paper presents a design of sliding mode control with a modified surface, type Proportional Integral Derivative (PID), optimized by the teaching learning-based optimization algorithm (TLBO-PID-SMC) to control the speed wind turbine. The TLBO algorithm is applied in this paper to tune optimally of the three parameters (Kp, Ki, Kd) of the PID sliding surface. The principal objective of the proposed controller is to eliminate the chattering presented in the conventional sliding mode control and for enhancing the tracking error performance which guarantees a maximum wind power extraction. The developed sliding mode controller is applied in order to the actual trajectory follows the required trajectory in spite of presence of the uncertainties, and nonlinear dynamics. The Stability of the wind turbine system is mathematically proved by Lyapunov theorem. The developed method is compared with the PID-SMC and the conventional SMC, the results of simulations show the better tracking performance of the developed method (TLBO-PID-SMC) with less oscillations and quick settling time compared to other controller which demonstrates the validity and the stability of the developed method even with the uncertainties.

关键词： Sliding mode control PID Sliding Surface Variable speed wind turbine teaching learning-based optimization

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