Cosmological parameter estimation using Particle Swarm Optimization
  (PSO)
  Obtaining the set of cosmological parameters consistent with observational
data is an important exercise in current cosmological research. It involves
finding the global maximum of the likelihood function in the multi-dimensional
parameter space. Currently sampling based methods, which are in general
stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly
used for parameter estimation. The beauty of stochastic methods is that the
computational cost grows, at the most, linearly in place of exponentially (as
in grid based approaches) with the dimensionality of the search space. MCMC
methods sample the full joint probability distribution (posterior) from which
one and two dimensional probability distributions, best fit (average) values of
parameters and then error bars can be computed. In the present work we
demonstrate the application of another stochastic method, named Particle Swarm
Optimization (PSO), that is widely used in the field of engineering and
artificial intelligence, for cosmological parameter estimation from WMAP seven
years data. We find that there is a good agreement between the values of the
best fit parameters obtained from PSO and publicly available code COSMOMC.
However, there is a slight disagreement between error bars mainly due to the
fact that errors are computed differently in PSO. Apart from presenting the
results of our exercise, we also discuss the merits of PSO and explain its
usefulness in more extensive search in higher dimensional parameter space.
