Particle swarm optimization in constrained maximum likelihood estimation a case study
This addresses optimization challenges in bioinformatics for pseudotime analysis, but it is incremental as it applies existing PSO variants to a specific domain problem.
The paper tackled constrained maximum likelihood estimation in pseudotime analysis by applying global best and local best particle swarm optimization, showing it is extremely useful and efficient for non-differentiable and non-convex optimization problems where analytical solutions and gradient-based methods fail.
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.