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Swarm Intelligence in Cosmology and In Black Hole Study

Author(s) Dr. PRASEETHA PANKUNNI, Dr. NATASHA PANKUNNI
Country India
Abstract As Swarm Intelligence implies collective behavior of systems seen in the nature, it has applications in the study of universe which has numerous evolutionary stages comprising varied cosmological structures. Cosmology is considered to the study of the origin and evolution of the universe, in which the galaxies, comprising millions of stars and interstellar matter and the cluster structures pervaded over the space called as the large-scale structure can also be viewed as the Cosmic web, implying existence of interconnected network among those anisotropic structures. Swarm intelligence based computational method is used as a tool to study these large-scale structures, in which the algorithm models and extracts one dimensional structure. Moreover, in the process of estimating cosmological parameters, in the case of the theoretical cosmological model, Λ-Cold Dark Matter Model (LCDM), Particle Swarm Optimization (PSO) tool, which is based on the artificial intelligence technology is used. Black holes are singularities existing in space-time, and are intensive gravitating cosmic bodies from where even light cannot escape out of them. Their existence being proven recently through gravitational wave detection observations, the study of their dynamics is a subject of interest for researchers. In consideration with the black hole theory, based on its power of pulling masses towards it as they pass nearby, a newer kind of PSO algorithm is developed called as the Random Black Hole Particle Swarm Optimization (RBH-PSO) (Zhang, Liu, Tan, & He, 2008). The present study is a conceptual paper intended to explore the possibilities of swarm intelligence in cosmology.
Keywords Black hole, Cosmology, Λ-Cold Dark Matter Model, Particle Swarm Optimization (PSO), Swarm Intelligence
Field Physics
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-07-10
DOI https://doi.org/10.71097/IJSAT.v16.i3.6922
Short DOI https://doi.org/g9sx5g
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