Paper Info
Title
Memetic algorithms applied to the optimization of workflow compositions.
Abstract
The selection of services of a workflow based on Quality of Service (QoS) attributes is an important issue in service-oriented systems. QoS attributes allow for a better selection process based on non-functional quality criteria such as reliability, availability, and response time. Past research has mostly addressed this problem with optimal methods such as linear programming approaches. Given the nature of service-oriented systems where large numbers of services are available with different QoS values, optimal methods are not suitable and therefore, approximate techniques are necessary. In this paper, we investigate Genetic algorithms and particle swarm optimization for the service selection process. In particular, both methods are combined with an optimal assignment algorithm (Munkres algorithm) in order to achieve higher solution qualities (success ratios) and to form a so called memetic algorithm. Experiments are conducted to investigate the suitability of the approaches and to compare the memetic algorithms with their non-memetic counterparts. The results reveal that the memetic algorithms are very suitable for the application to the workflow selection problem.
Year
DOI
Venue
2013
10.1016/j.swevo.2012.12.001
Swarm and Evolutionary Computation
Keywords
Field
DocType
Genetic algorithms,Particle swarm optimization,Munkres algorithm,Quality of service,Memetic algorithms
Hungarian algorithm,Memetic algorithm,Particle swarm optimization,Mathematical optimization,Computer science,Response time,Quality of service,Linear programming,Workflow,Genetic algorithm
Journal
Volume
ISSN
Citations 
10
2210-6502
2
PageRank 
References 
Authors
0.40
26
1
Name
Order
Citations
PageRank
Simone A Ludwig11309179.41