Particle swarm optimization in constrained maximum likelihood estimation a case study

Elvis Cui; Dongyuan Song; Weng Kee Wong

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arXiv · 2104.10041

Particle swarm optimization in constrained maximum likelihood estimation a case study

Elvis Cui·Dongyuan Song·Weng Kee Wong

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Abstract

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.

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Elvis Cui, Dongyuan Song, Weng Kee Wong. 2021-04-09. Particle swarm optimization in constrained maximum likelihood estimation a case study. https://arxiv.org/abs/2104.10041

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