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Phase transitions in swarm optimization algorithms

Adamatzky, Andrew

Authors



Contributors

Susan Stepney
Editor

Sergey Verlan
Editor

Abstract

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarms optimisation algorithms also exhibit transitions from chaos, analogous to motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyse these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging and non-converging iterations of the optimization algorithms are statistically different in a view of applied chaos, complexity and predictability estimating indicators.

Publication Date Jul 1, 2018
Peer Reviewed Peer Reviewed
Pages 204-2016
Book Title Unconventional Computation and Natural Computation
ISBN 9783319924359
APA6 Citation Adamatzky, A. (2018). Phase transitions in swarm optimization algorithms. In S. Stepney, & S. Verlan (Eds.), Unconventional Computation and Natural Computation, 204-2016. Spinrger
Keywords unconventional computation, optimization
Publisher URL https://www.springer.com/us/book/9783319924342
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