Particle swarm optimization in constrained maximum likelihood estimation a case study
Neural and Evolutionary Computing
2022-10-04 v1 Artificial Intelligence
Applications
Computation
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.
Cite
@article{arxiv.2104.10041,
title = {Particle swarm optimization in constrained maximum likelihood estimation a case study},
author = {Elvis Cui and Dongyuan Song and Weng Kee Wong},
journal= {arXiv preprint arXiv:2104.10041},
year = {2022}
}
Comments
11 pages, 7 figures