Hybrid glowworm swarm optimization for task scheduling in the cloud environment. Issue 6 (3rd June 2018)

• Record Type: Journal Article
• Authors: Zhou, Jing; Dong, Shoubin
• Is Part Of: Engineering optimization. Volume 50:Issue 6(2018)
• Page Start: 949
• Page End: 964
• Publication Date: 2018-06-03
• Subjects: Hybrid metaheuristic -- swarm intelligence -- glowworm swarm optimization -- task scheduling -- cloud computing; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1361418
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.