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

Abstract

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.