Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors – updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly – attractors can draw other particles towards them, and these particles can update their own personal bests to be near the first attractor. This convergence of attractors can be reduced by having a particle update the attractor it has approached rather than its own attractor/personal best. This simple change to the update procedure in particle swarm optimization incurs minimal computational cost, and it can lead to large performance improvements in multi-modal search spaces.
History
Publication title
AI 2011: Advances in Artificial Intelligence
Editors
D Wang, M Reynolds
Pagination
281-290
ISBN
978-3-642-25831-2
Department/School
School of Information and Communication Technology
Publisher
Springer-Verlag
Place of publication
Berlin, Germany
Event title
24th Australasian Joint Conference on Artificial Intelligence
Event Venue
Perth, Australia
Date of Event (Start Date)
2011-12-05
Date of Event (End Date)
2011-12-08
Rights statement
Copyright 2011 Springer
Repository Status
Restricted
Socio-economic Objectives
Expanding knowledge in the information and computing sciences