Abstract
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.
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Röhler, A.B., Chen, S. (2011). An Analysis of Sub-swarms in Multi-swarm Systems. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_28
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DOI: https://doi.org/10.1007/978-3-642-25832-9_28
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