Cosmological Parameter Estimation Using Particle Swarm Optimization

The quest for a theoretical framework and ingredients that capture our current understanding of the cosmos has motivated the design of a large number of highly informative experiments, generating an abundant flow of data. Given this quantity of data and the need for thorough analysis, the main aim of this work is to present and assess the Particle Swarm Optimization (PSO) algorithm as a complementary tool to conventional cosmological data analysis techniques. PSO is one of the most representative bio-inspired algorithms, offering good robustness for high-dimensional or complex problems while remaining relatively simple to implement and requiring only a few hyperparameters. In this study, we employ two standard variants of the canonical PSO algorithm—global best and local best—to investigate dark energy models using measurements of Type Ia Supernovae and Baryon Acoustic Oscillations, focusing in particular on the DESI and DESI + Union3 datasets. Our findings demonstrate that PSO effectively recovers the best-fit parameters from observational data and show that, under suitable conditions, PSO can achieve results comparable to those of traditional MCMC techniques, but in a significantly reduced computation time. Moreover, the solutions obtained with PSO can be used as high-quality initial conditions for MCMC analyses, thereby accelerating their convergence.