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23 February 2005 Possibilistic particle swarms for optimization
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Abstract
We present a new approach for extending the particle swarm optimization algorithm to multi-optima problems by using ideas from possibility theory. An elastic constraint is used to let the particles dynamically explore the solution space in two phases. In the exploratory phase, particles explore the space in an effort to track the global minima while also traversing the local minima. In the exploitatory phase, particles disperse in the local neighborhoods to locate the best local minima. The proposed PPSO has been applied to data clustering and object detection. Our preliminary results indicate that the proposed approach is efficient and robust.
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Swarup Medasani and Yuri Owechko "Possibilistic particle swarms for optimization", Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); https://doi.org/10.1117/12.588353
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