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Particle swarm optimization with thresheld convergence
conference contribution
posted on 2023-05-23, 08:56 authored by Chen, S, Erin MontgomeryErin MontgomeryMany heuristic search techniques have concurrent processes of exploration and exploitation. In particle swarm optimization, an improved pbest position can represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). The latter can interfere with the former since the identification of a new more promising region depends on finding a (random) solution in that region which is better than the current pbest. Ideally, every sampled solution will have the same relative fitness with respect to its nearby local optimum – finding the best region to exploit then becomes the problem of finding the best random solution. However, a locally optimized solution from a poor region of the search space can be better than a random solution from a good region of the search space. Since exploitation can interfere with subsequent/concurrent exploration, it should be prevented during the early stages of the search process. In thresheld convergence, early exploitation is “held” back by a threshold function. Experiments show that the addition of thresheld convergence to particle swarm optimization can lead to large performance improvements in multi-modal search spaces.
History
Publication title
Proceedings of the 2013 IEEE Congress on Evolutionary ComputationPagination
510-516ISBN
978-1-4799-0453-2Department/School
School of Information and Communication TechnologyPublisher
IEEEPlace of publication
United States of AmericaEvent title
2013 IEEE Congress on Evolutionary ComputationEvent Venue
Cancun, MexicoDate of Event (Start Date)
2013-06-20Date of Event (End Date)
2013-06-23Rights statement
Copyright 2012 IEEERepository Status
- Restricted