Affiliations: [a] College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010020, China | [b] Key Laboratory of Research on Hydraulic and Hydro-Power Equipment Surface Engineering Technology of Zhejiang, Zhejiang 31002, China
Correspondence:
[*]
Corresponding author: Xue-Liang Fu, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010020, China. E-mail: [email protected].
Abstract: In the field of cloud computing, Particle swarm optimization (PSO) is an important intelligent algorithm for solving the task scheduling problem, and has been rapidly developed. In order to improve the overall optimization ability, and get a low cost optimization solution, this paper proposes an improved particle swarm optimization (IPSO) algorithm based on the adaptive inertia weight and random factor correlation. Simulation results show that under the same conditions, IPSO algorithm is less than the sequential scheduling algorithm, the greedy algorithm, the correlation particle swarm optimization (CPSO) algorithm and the new adaptive inertia weight based particle swarm optimization (NewPSO) algorithm in terms of cost consumption (including time cost and virtual machine cost).
