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Title

Population-ACO for the automotive deployment problem

Author(s)

Moser, Irene;  Montgomery, James

Abstract

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.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011), a recombination of the 20th International Conference on Genetic Algorithms (ICGA) and the 16th Annual Genetic Programming Conference (GP), Dublin, Ireland, 12-16 July 2011 / Natalio Krasnogor (ed.), pp. 777-784

Publication year

2011

FOR Code(s)

08 Information and Computing Sciences;  09 Engineering

Keyword(s)

ACO;  Algorithms;  Ant colony optimisation;  Automotive deployment;  Artificial intelligence;  Constrained problem;  Embedded systems;  Genetic algorithms;  Local search;  Multiobjective problem;  Optimisation;  Population-based ant colony optimisation;  Problem solving

Publisher

ACM

ISBN

9781450305570

Publisher URL

http://dx.doi.org/10.1145/2001576.2001682

Copyright

Copyright © ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the proceedings of GECCO, (2011) http://doi.acm.org/10.1145/2001576.2001682.

Full text

Peer reviewed