  The aim of paper is to apply two types of particle swarm optimization, global
best andlocal best PSO to a constrained maximum likelihood estimation problem
in pseudotime anal-ysis, a sub-field in bioinformatics. The results have shown
that particle swarm optimizationis extremely useful and efficient when the
optimization problem is non-differentiable and non-convex so that analytical
solution can not be derived and gradient-based methods can not beapplied.
