Survey on Task Scheduling with Ant Colony Optimization

Ant Colony Optimization(ACO) is considered as a high level problem independent algorithm that is increasingly applied to solve complex task scheduling problems in various domains. It is an algorithm that draws inspiration from nature and is frequently used to address task scheduling issues in a variety of fields. ACO algorithms discover the best routes from a starting point to a destination by modelling the behaviour of actual ant colonies. In this paper, we provide a comprehensive survey of ACO task scheduling, putting up the advantages, limitations, and challenges on this approach. We review the basic principles and variations of ACO algorithm, as well as their applications to different scheduling objectives and constraints. We also discuss the performance comparison of ACO with other scheduling algorithms and the optimization of ACO parameters. Furthermore, we identify the future research directions and potential applications of ACO task scheduling, such as hybridization with other algorithms and integration with cloud computing. Our survey provides insights into the leading edge research on ACO task scheduling and highlights the potential of this approach to address the increasing demand for efficient and effective task scheduling solutions.