A velocity-combined local best particle swarm optimization algorithm for nonlinear equations
type: Item
instance of: scholarly article
title: A velocity-combined local best particle swarm optimization algorithm for nonlinear equations
zbMATH Open document ID: 1459.65063
DOI: 10.1155/2020/6284583
author: Zhigang Lian
author: Songhua Wang
author: Yangquan Chen
published in: Mathematical Problems in Engineering
publication date: 2020-10-12T00:00:00Z
review text: Summary: Many people use traditional methods such as quasi-Newton method and Gauss-Newton-based BFGS to solve nonlinear equations. In this paper, we present an improved particle swarm optimization algorithm to solve nonlinear equations. The novel algorithm introduces the historical and local optimum information of particles to update a particle's velocity. Five sets of typical nonlinear equations are employed to test the quality and reliability of the novel algorithm search comparing with the PSO algorithm. Numerical results show that the proposed method is effective for the given test problems. The new algorithm can be used as a new tool to solve nonlinear equations, continuous function optimization, etc., and the combinatorial optimization problem. The global convergence of the given method is established.
Mathematics Subject Classification ID: 65H10
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7259636
describes a project that uses: ANTIGONE
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/6284583
OpenAlex ID: W3081317634
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description: scientific article; zbMATH DE number 7259636