Phase transitions in swarm optimization algorithms
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910905339478016 |
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| author | Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert |
| author_facet | Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase transitions in swarm optimization algorithms Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Computational Physics Chaotic Dynamics Data Analysis, Statistics and Probability 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. |
| title | Phase transitions in swarm optimization algorithms |
| topic | Computational Physics Chaotic Dynamics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2504.04947 |