Self-Adaptive Ant Colony Optimisation Applied to Function Allocation in Vehicle Networks
Förster M, Bickel B, Hardung B, Kókai G (2007)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2007
Edited Volumes: Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
Pages Range: 1991-1998
Conference Proceedings Title: Proceeding Genetic and Evolutionary Computation Conference (GECCO-2007)
Event location: London
ISBN: 978-1-59593-697-4
URI: http://www2.informatik.uni-erlangen.de/publication/download/paperGECCO2007.pdf
Abstract
Modern vehicles possess an increasing number of softwareand hardware components that are integrated in electroniccontrol units (ECUs). Finding an optimal allocation forall components is a multi-objective optimisation problem,since every valid allocation can be rated according to multipleobjectives like costs, busload, weight, etc. Additionally,several constraints mainly regarding the availability of resourceshave to be considered. This paper introduces a newvariant of the well-known ant colony optimisation, whichhas been applied to the real-world problem described above.Since it concerns a multi-objective optimisation problem,multiple ant colonies are employed. In the course of thiswork, pheromone updating strategies specialised on constrainthandling are developed. To reduce the effort neededto adapt the algorithm to the optimisation problem by tuningstrategic parameters, self-adaptive mechanisms are establishedfor most of them. Besides the reduction of theeffort, this step also improves the algorithm's convergencebehaviour. Copyright 2007 ACM.
How to cite
APA:
Förster, M., Bickel, B., Hardung, B., & Kókai, G. (2007). Self-Adaptive Ant Colony Optimisation Applied to Function Allocation in Vehicle Networks. In Proceeding Genetic and Evolutionary Computation Conference (GECCO-2007) (pp. 1991-1998). London, GB.
MLA:
Förster, Manuel, et al. "Self-Adaptive Ant Colony Optimisation Applied to Function Allocation in Vehicle Networks." Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2007), London 2007. 1991-1998.
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