Redefining teaching-and-learning-process in TLBO and its application in cloud

Ram, Satya Deo Kumar; Srivastava, Shashank; Mishra, K. K.

doi:10.1016/j.asoc.2023.110017

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…A scheduling heuristic describes how I‐TLBO is used for scheduling the workflow. Scheduling heuristics (Reference 71, Algorithm 1) guide the I‐TLBO meta‐heuristic algorithm to generate particles and evaluate the fitness of particles, finally iterating up to some generation to produce an optimal mapping. It is followed to evaluate the average processing cost for all tasks‐to‐VMs and the total transfer cost among all VMs.…”

Section: Examining I‐tlbo For Workflow Scheduling Problemmentioning

confidence: 99%

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Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Ram

Srivastava

Mishra

2023

Concurrency and Computation

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SummaryTeaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐heuristic algorithm that was created to solve single‐objective optimization problems. The teaching‐learning mechanism of a classroom inspires it. TLBO suffers from weak exploration. As a result, its performance is not good for solving multimodal problems. To turn TLBO into a tool for solving multimodal problems and maintaining good diversity, we made significant modifications into the learning process of the fundamental TLBO. The proposed algorithm produces more diverse solutions and works better for solving multimodal problems. This newly created variant of TLBO is called “Intelligent‐Teaching‐Learning‐Based Optimization (I‐TLBO) algorithm.” I‐TLBO's performance is evaluated against the most recent standard benchmark function, CEC‐06, 2019, and it is discovered that I‐TLBO outperforms the other algorithms. After that, I‐TLBO was applied for flowtime‐aware‐cost minimization of the workflow executions in cloud datacenter. To solve these scheduling problems, I‐TLBO and other metaheuristic algorithms are simulated in CloudSim and tested over scientific workflows such as Inspiral, Montage, SIPHT, sample, Cybershake, and Epigenomics workflows. Finally, it is found that I‐TLBO reduces flowtime and cost both by 28.48%, 11.30%, 17.64%, 13.22%, 11.45%, and 14.71% in comparison to the second best performing algorithm while executing the standard workflow in cloud.

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Section: Examining I‐tlbo For Workflow Scheduling Problemmentioning

confidence: 99%

“…All line numbers from 1 to 4 are followed to do all the necessary cost‐calculation. At line 5 (Reference 71, Algorithm 1), I‐TLBO is iterated instead of MT‐TLBO. Further, lines 5 to 14 show how I‐TLBO accepts inputs and produces the desired optimal mapping.…”

Section: Examining I‐tlbo For Workflow Scheduling Problemmentioning

confidence: 99%

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Ram

Srivastava

Mishra

2023

Concurrency and Computation

Self Cite

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“…Mohammad et al used a modified dynamic oppositional learning strategy and proposed a modified dynamic oppositional learning TLBO (MDOLTLBO) algorithm [14]. Satya et al redefined the learning strategies and proposed a multi-teacher TLBO (MT-TLBO) [15]. The third category consists of hybrid algorithms that combine TLBO with other optimization algorithms or search techniques.…”

Section: Introductionmentioning

confidence: 99%

Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application

Ma,

Li,

Yong

2024

Mathematics

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This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm’s superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method.

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Teaching-learning-based optimization algorithm with dynamic neighborhood and crossover search mechanism for numerical optimization

Zeng,

Dong,

et al. 2024

Applied Soft Computing

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Redefining teaching-and-learning-process in TLBO and its application in cloud

Cited by 7 publications

References 59 publications

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application

Teaching-learning-based optimization algorithm with dynamic neighborhood and crossover search mechanism for numerical optimization

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