Work

Weak convergence of particle swarm optimization

Year: 2018

Type: preprint

Abstract: Particle swarm optimization algorithm is a stochastic meta-heuristic solving global optimization problems appreciated for its efficacity and simplicity. It consists in a swarm of particles interacting among themselves and searching the global optimum. The trajectory of the particles has been well-studied in a deterministic case and more recently in a stochastic context. Assuming the convergence of PSO, we proposed here two CLT for the particles corresponding to two kinds of convergence behavior. These results can lead to build confidence intervals around the local minimum found by the swarm or to the evaluation of the risk. A simulation study confirms these properties.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1811.04924

Authors: Bruned, Vianney, Mas, André, Wlodarczyk, Sylvain

Institutions: Centre National de la Recherche Scientifique, Institut Montpelliérain Alexander Grothendieck, Université Montpellier 2

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2901259238

Cites: 19

Cited by: 3

Related to: 10