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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 Montgomery
Many 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.


Publication title

Proceedings of the 2013 IEEE Congress on Evolutionary Computation






School of Information and Communication Technology



Place of publication

United States of America

Event title

2013 IEEE Congress on Evolutionary Computation

Event Venue

Cancun, Mexico

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2012 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the information and computing sciences

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