Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center

EasyChair Preprint 11312

Abstract

This paper proposes an improved particle swarm optimization algorithm with simulated annealing (IPSO-SA) for the task scheduling problem of cloud data center. The algorithm uses Tent chaotic mapping to make the initial population more evenly distributed. Secondly, nonlinear adaptive inertia weights is incorporated to adjust optimization seeking capabilities of particles in different iteration periods. Finally, the Metropolis criterion in SA is used to generate perturbed particles, combined with an modified equation for updating particles to avoid premature particle convergence. Comparative experimental results show that the IPSO-SA algorithm improves 13.8% in convergence accuracy over the standard PSO algorithm. The respective improvements over the other two modified PSO are 15.2% and 9.1%.

Keyphrases: Cloud Data Center, Metropolis Criterion, Particle Swarm Optimization, Simulated Annealing, Tent map, task scheduling

@booklet{EasyChair:11312,
  author    = {Yang Bi and Wenlong Ni and Yao Liu and Lingyue Lai and Xinyu Zhou},
  title     = {Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center},
  howpublished = {EasyChair Preprint 11312},
  year      = {EasyChair, 2023}}