Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

Rong Zhang, Feng Tian, Xiaochun Ren, Yaxing Chen, Kuoming Chao, Ruomeng Zhao, Bo Dong, Wei Wang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Random search-based scheduling algorithms, such as particle swarm optimization (PSO), are often used to solve independent multi-task scheduling problems in cloud, but the quality of optimal solution of the algorithm often has greater deviation and poor stability when the tasks are associate. In this paper, we propose an algorithm called SADCPSO to solve this challenging problem, which improves the PSO algorithm by uniquely integrating the self-adaptive inertia weight, disruption operator and chaos operator. In particular, the self-adaptive inertia weight is adopted to adjust the convergence rate, the disruption operator is applied to prevent the loss of population diversity, and the chaos operator is introduced to prevent the solution from tending to jump into the local optimal. Furthermore, we also provide a scheme to apply the SADCPSO algorithm to solve the associate multi-task scheduling problem.
Original languageEnglish
Pages (from-to)87-94
Number of pages8
JournalService Oriented Computing and Applications
Volume12
Issue number2
Early online date23 Mar 2018
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • Associate task
  • Chaos
  • Cloud computing
  • Disruption
  • Particle swarm optimization

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