Title: Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Authors: Gang Dai; Baomin Xu; Jianfeng Peng; Lei Zhang

Addresses: School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China; China University of Petroleum-Beijing (KARAMAY), Karamay City, Xinjiang, China ' School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China ' China Railway Information Engineering Group Co., Ltd. (SINORAIL), Beijing, China ' School of Computer Science, Beijing Jiaotong University (Haibin College), Hebei, Huanghua, China

Abstract: Particle swarm optimisation has been widely used in solving scheduling problems. This paper proposes a hybrid algorithm namely Hill Climbing with Multi-objective Particle Swarm Optimisation called HCMOPSO, which is based on heuristic local search and multi-objective particle swarm optimisation algorithm. HCMOPSO introduces hill climbing optimisation techniques into particle swarm optimisation algorithm to improve the local search ability. Experimental results show that the HCMOPSO is an effective cloud workflow scheduling algorithm, which has faster convergence velocity and better optimisation ability.

Keywords: hill climbing algorithm; task scheduling; particle swarm optimisation; cloud workflow.

DOI: 10.1504/IJGUC.2021.117850

International Journal of Grid and Utility Computing, 2021 Vol.12 No.3, pp.287 - 301

Received: 20 Nov 2019
Accepted: 10 Jun 2020
Published online: 04 Oct 2021 *

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