Using a distance metric to guide PSO algorithms for many-objective optimization

Wickramasinghe Rajapaksa, U and Li, X 2009, 'Using a distance metric to guide PSO algorithms for many-objective optimization', in Günther Raidl et al. (ed.) GECCO 2009 Proceedings, Montreal, Canada, 8-12 July 2009, pp. 667-674.


Document type: Conference Paper
Collection: Conference Papers

Title Using a distance metric to guide PSO algorithms for many-objective optimization
Author(s) Wickramasinghe Rajapaksa, U
Li, X
Year 2009
Conference name Genetic and Evolutionary Computation Conference (GECCO 2009)
Conference location Montreal, Canada
Conference dates 8-12 July 2009
Proceedings title GECCO 2009 Proceedings
Editor(s) Günther Raidl et al.
Publisher ACM
Place of publication New York, U.S.A
Start page 667
End page 674
Total pages 8
Abstract In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. We use a particle swarm optimization (PSO) algorithm as a baseline to demonstrate the usefulness of this distance metric, though the metric can be used in conjunction with any evolutionary multi-objective (EMO) algorithm. Existing user-preference based EMO algorithms rely on the use of dominance comparisons to explore the search-space. Unfortunately, this is ineffective and computationally expensive for many-objective problems. In the proposed distance metric based PSO, particles update their positions and velocities according to their closeness to preferred regions in the objective-space, as specified by the decision maker. The proposed distance metric allows an EMO algorithm's search to be more effective especially for many-objective problems, and to be more focused on the preferred regions, saving substantial computational cost. We demonstrate how to use a distance metric with two user-preference based PSO algorithms, which implement the reference point and light beam search methods. These algorithms are compared to a user-preference based PSO algorithm relying on the conventional dominance comparisons. Experimental results suggest that the distance metric based algorithms are more effective and efficient especially for difficult many-objective problems
Subjects Optimisation
Analysis of Algorithms and Complexity
Keyword(s) Particle swarm optimization
Many-objective optimization
User-preference methods
Reference point method
Light beam search
Copyright notice © 2009 ACM
ISBN 9781605583259
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