Convergence analysis of Particle Swarm Optimization in one dimension

Young Pil Choi; Hyeonho Ju; Dowan Koo

doi:10.1016/j.aml.2022.108481

Convergence analysis of Particle Swarm Optimization in one dimension

Young Pil Choi, Hyeonho Ju, Dowan Koo

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper is devoted to a rigorous analysis of one dimensional Particle Swarm Optimization (PSO) for non convex objective function. We provide a quantitative bound on the error between the equilibrium point and the global minimizer of the given objective function. Our strategy does not rely on the Laplace principle. We also investigate the underdamped PSO with linear objective function.

Original language	English
Article number	108481
Journal	Applied Mathematics Letters
Volume	137
DOIs	https://doi.org/10.1016/j.aml.2022.108481
Publication status	Published - 2023 Mar

Bibliographical note

Funding Information:
Acknowledgment: YPC has been supported by NRF, Republic of Korea grant (No. 2022R1A2C1002820 ) and Yonsei University Research Fund of 2021-22-0301 .

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Applied Mathematics

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10.1016/j.aml.2022.108481

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abstract = "This paper is devoted to a rigorous analysis of one dimensional Particle Swarm Optimization (PSO) for non convex objective function. We provide a quantitative bound on the error between the equilibrium point and the global minimizer of the given objective function. Our strategy does not rely on the Laplace principle. We also investigate the underdamped PSO with linear objective function.",

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Convergence analysis of Particle Swarm Optimization in one dimension

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