Nature Inspired Meta-heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches

DOI10.1007/978-3-540-69277-5_9zbMath1153.68333OpenAlexW172801870WikidataQ61736656 ScholiaQ61736656MaRDI QIDQ3603117

Ajith Abraham, Crina Grosan, Fatos Xhafa, Hong Bo Liu

Publication date: 13 February 2009

Published in: Studies in Computational Intelligence (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2117/128951

zbMATH Keywords

multi-objective optimizationsimulated annealinggenetic algorithmsjob schedulinggrid computingparticle swarm optimizationant colonynature inspired meta-heuristics

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Performance evaluation, queueing, and scheduling in the context of computer systems (68M20)

Related Items (3)

Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems ⋮ EFFICIENT GRID SCHEDULING THROUGH THE INCREMENTAL SCHEDULE-BASED APPROACH ⋮ Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Cites Work

• Optimization by Simulated Annealing
• Recent approaches to global optimization problems through particle Swarm optimization
• Evolutionary multiobjective optimization. Theoretical advances and applications.
• Particle Swarm Optimization
• A new simulated annealing algorithm
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Nature Inspired Meta-heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches