Abstract Teaching-Learning-Based Optimization (TLBO) algorithm was developed to solve
single-objective optimization problems. TLBO is inspired by the theory of teaching-learning …

Resource allocation is a non-polynomial complete problem in the cloud data center that
selects the proper resources to execute many fine computational granularity tasks. Customer …

This paper proposes a novel social cognitive learning-based metaheuristic called kids
Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized …

Abstract Teaching–Learning-Based Optimization (TLBO) was developed to solve single-
objective optimization problems. It is inspired by the theory of teaching–learning mechanism …

Recently, cloud computing is growing rapidly and contributing to the realization of other
technologies. Cloud data centers use a lot of energy to host applications, which leads to …

Although cloud computing can provide information technology services worldwide, data
centers hosting cloud applications consume a lot of energy. At the same time, the ever …

Teaching-learning-based optimization (TLBO) is an optimization algorithm that has become
very popular in recent years and has shown excellent performance in solving many scientific …

At present, workflow scheduling in cloud computing environment is still a challenging
optimization topic due to its NP-complete characteristics. In order to obtain better scheduling …

Workflow scheduling is an important way to manage the execution of a workflow. It
introduces the concept of providing suitable resources to workflow tasks in order to finish …

Teaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐
heuristic algorithm that was created to solve single‐objective optimization problems. The …