05993389

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05993389 R\"ohler, Antonio Boluf\'e Chen, Stephen An analysis of sub-swarms in multi-swarm systems. Wang, Dianhui (ed.) et al., AI 2011: Advances in artificial intelligence. 24th Australasian joint conference, Perth, Australia, December 5--8, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-25831-2/pbk). Lecture Notes in Computer Science 7106. Lecture Notes in Artificial Intelligence, 271-280 (2011). 2011 Berlin: Springer EN particle swarm optimization exploration-exploitation multi-swarm system multi-modal search spaces

doi:10.1007/978-3-642-25832-9_28

Summary: Particle swarm optimization cannot guarantee convergence to the global optimum on multi-modal functions, so multiple swarms can be useful. One means to coordinate these swarms is to use a separate search mechanism to identify different regions of the solution space for each swarm to explore. The expectation is that these independent sub-swarms can each perform an effective search around the region where it is initialized. This regional focus means that sub-swarms will have different goals and features when compared to standard (single) swarms. A comprehensive study of these differences leads to a new set of general guidelines for the configuration of sub-swarms in multi-swarm systems.