Task Scheduling Using Constriction Weighted Particle Swarm Optimization for Multi-Objectives

Vidya, G.; Sarathambekai, S.; Umamaheswari, K.; Yamunadevi, S.

doi:10.1016/j.proeng.2012.06.355

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…However, when load is high the strategy is deficient in terms making tradeoff between user task satisfaction and scheduling time. Vidya et al (2012) presented a strategy based on constriction factor where the constriction is to eliminate the inertia weight. However, this approach deficient in terms of its encoding scheme as particle is represented as entire solution set.…”

Section: Fig 2 Distribution Of Scheduling Metricsmentioning

confidence: 99%

A Survey: Particle Swarm Optimization-Based Algorithms for Grid Computing Scheduling Systems

Ambursa

Latip

2013

Journal of Computer Science

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Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Metatask-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category.

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Section: Fig 2 Distribution Of Scheduling Metricsmentioning

confidence: 99%