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A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments.

Yousef Qawqzeh1, Mafawez T Alharbi2, Ayman Jaradat3

  • 1Department of Computer Science and Engineering, Hafr Al Batin University, Hafr AL Batin, Saudi Arabia.

Peerj. Computer Science
|September 20, 2021
PubMed
Summary
This summary is machine-generated.

Swarm intelligence (SI) algorithms, including particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and firefly algorithm (FA), are increasingly applied to cloud computing scheduling and optimization. Research shows a rise in SI algorithm publications, with many new methods based on PSO modifications.

Keywords:
Cloud ComputingOptimizationSchedulingSwarm IntelligenceTask-Allocation

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Nature-Inspired Computing

Background:

  • Swarm intelligence (SI) mimics collective behavior in natural systems like insect and animal swarms.
  • SI algorithms utilize self-organized agents to solve complex computational problems effectively.
  • Key SI algorithms include particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and firefly algorithm (FA).

Purpose of the Study:

  • To review recent publications (2015-2021) on swarm intelligence algorithms in scheduling and optimization.
  • To summarize the application of SI techniques in cloud computing environments for optimal scheduling strategies.

Main Methods:

  • Systematic review of recent scientific literature on SI algorithms.
  • Focus on publications from 2015 to 2021.
  • Analysis of SI algorithm applications in cloud computing optimization and scheduling.

Main Results:

  • A significant increase in the number of publications related to SI algorithms for cloud computing optimization.
  • Growing research interest in particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and firefly algorithm (FA).
  • Emergence of new algorithms, often based on modifications of existing SI algorithms, particularly PSO.

Conclusions:

  • The field of SI algorithms for cloud computing optimization is rapidly expanding.
  • There is a notable trend towards developing enhanced SI algorithms, especially those derived from PSO.
  • Encourages further research and innovation in developing novel SI-based solutions for complex, multi-objective computational problems.