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Selection strategies for initial positions and initial velocities in multi-optima particle swarms


Chen, S and Montgomery, J, Selection strategies for initial positions and initial velocities in multi-optima particle swarms, Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 12-16 July 2011, Dublin, Ireland, pp. 53-60. ISBN 978-1450312547 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 ACM

DOI: doi:10.1145/2001576.2001585


Standard particle swarm optimization cannot guarantee convergence to the global optimum in multi-modal search spaces, so multiple swarms can be useful. The multiple swarms all need initial positions and initial velocities for their particles. Several simple strategies to select initial positions and initial velocities are presented. A series of experiments isolates the effects of these selected initial positions and velocities compared to random initial positions and velocities. A first set of experiments shows how locust swarms benefit from "scouting" for initial positions and the use of initial velocities that "launch away" from the previous optimum. A second set of experiments show that the performance of WoSP (Waves of Swarm Particles) can be improved by using new search strategies to select the initial positions and initial velocities for the particles in its sub-swarms.

Item Details

Item Type:Refereed Conference Paper
Keywords:particle swarm optimization, coarse search-greedy search, exploration-exploitation, locust swarms, WoSP, multi-swarm system
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:92110
Year Published:2011
Web of Science® Times Cited:14
Deposited By:Information and Communication Technology
Deposited On:2014-06-06
Last Modified:2018-01-28

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