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Population-ACO for the automotive deployment problem


Moser, I and Montgomery, J, Population-ACO for the automotive deployment problem, Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 12-16 July 2011, Dublin, Ireland, pp. 777-784. ISBN 978-1450312547 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 ACM

DOI: doi:10.1145/2001576.2001682


The automotive deployment problem is a real-world constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGA-II can produce good quality solutions to this problem. This paper presents a population-based ant colony optimisation (PACO) approach that uses a single pheromone memory structure and a range of local search operators. The PACO and prior NSGA-II are compared on two realistic problem instances. Results indicate that the PACO is generally competitive with NSGA-II and performs more effectively as problem complexity—size and number of objectives—is increased.

Item Details

Item Type:Refereed Conference Paper
Keywords:ant colony optimisation, automative deployment, constrained problem, embedded systems, genetic algorithms, local search, mutliobjective problem, optimisation
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:91930
Year Published:2011
Web of Science® Times Cited:6
Deposited By:Information and Communication Technology
Deposited On:2014-06-03
Last Modified:2018-03-09

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