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GECCO
2009
Springer

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

10 years 10 months ago
Using a distance metric to guide PSO algorithms for many-objective optimization
In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for manyobjective 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 searchspace. 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 h...
Upali K. Wickramasinghe, Xiaodong Li
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GECCO
Authors Upali K. Wickramasinghe, Xiaodong Li
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