Journal
ENGINEERING OPTIMIZATION
Volume 50, Issue 6, Pages 949-964Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2017.1361418
Keywords
Hybrid metaheuristic; swarm intelligence; glowworm swarm optimization; task scheduling; cloud computing
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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.
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