A hybrid chaotic quantum evolutionary algorithm for resource combinatorial optimization in manufacturing grid system

Resource composition in manufacturing grid (MGrid) system is one of the recent critical issues of MGrid researches. Especially, resource combinatorial optimization (RCO) becomes more challenging when multiple optimal criteria are considered in MGrid system. Based on the quantum evolution theory, we...

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Veröffentlicht in:International journal of advanced manufacturing technology Jg. 52; H. 5-8; S. 821 - 831
Hauptverfasser: Zhang, Haijun, Hu, Yefa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer-Verlag 01.02.2011
Springer Nature B.V
Schlagworte:
CAE) and Design
Combinatorial analysis
Computer-Aided Engineering (CAD
Engineering
Evolutionary algorithms
Genetic algorithms
Industrial and Production Engineering
Mechanical Engineering
Media Management
Optimization
Original Article
Chaotic search
Resource encoding
Quantum evolutionary algorithm
Manufacturing grid
Combinatorial optimization
ISSN:0268-3768, 1433-3015
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Zusammenfassung:Resource composition in manufacturing grid (MGrid) system is one of the recent critical issues of MGrid researches. Especially, resource combinatorial optimization (RCO) becomes more challenging when multiple optimal criteria are considered in MGrid system. Based on the quantum evolution theory, we propose a hybrid chaotic quantum evolutionary algorithm (CQEA) for RCO problems. We also propose a novel resource encoding method for CQEA, which is dynamic and flexible. The experimental results show that the proposed CQEA is effective, efficient, and scalable for the RCO problem in MGrid system.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-010-2742-z