# Particle swarm optimization in constrained maximum likelihood estimation a case study

arXiv: https://arxiv.org/abs/2104.10041 · HTML: https://mlsanity.com/paper/2104.10041/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 9 Apr 2021.

## Abstract (arXiv)

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.