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

Particle swarm optimization in constrained maximum likelihood estimation a case study