Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system

The workflow scheduling with multiple objectives is a well-known NP-complete problem, and even more complex and challenging when the workflow is executed in cloud computing system. In this study, an endocrine-based coevolutionary multi-swarm for multi-objective optimization algorithm (ECMSMOO) is pr...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 21; H. 15; S. 4309 - 4322
Hauptverfasser:	Yao, Guangshun, Ding, Yongsheng, Jin, Yaochu, Hao, Kuangrong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Cloud computing Computational Intelligence Computer centers Control Cooperation Customer services Design Employment Energy consumption Engineering Heuristic methods Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Objectives Optimization Particle swarm optimization Performance enhancement Resource scheduling Robotics Scheduling Task scheduling Workflow Multi-swarm Multi-objective workflow scheduling Endocrine regulation mechanism Cloud computing system Particle swarm optimization
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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