Possibilistic particle swarms for optimization

Swarup Medasani; Yuri Owechko

doi:10.1117/12.588353

23 February 2005 Possibilistic particle swarms for optimization

Swarup Medasani, Yuri Owechko

Proceedings Volume 5673, Applications of Neural Networks and Machine Learning in Image Processing IX; (2005) https://doi.org/10.1117/12.588353
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

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.

Citation Download Citation

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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