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A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations
Joint Authors
Wang, Songhua
Chen, Yangquan
Lian, Zhigang
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lian, Zhigang& Wang, Songhua& Chen, Yangquan. 2020. A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1196664
Modern Language Association (MLA)
Lian, Zhigang…[et al.]. A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations. Mathematical Problems in Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1196664
American Medical Association (AMA)
Lian, Zhigang& Wang, Songhua& Chen, Yangquan. A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1196664
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196664