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A finite-time particle swarm optimization algorithm for odor source localization

journal contribution
posted on 06.12.2017, 00:00 by Qiang Lu, Qing-Long Han, S Liu
This paper is concerned with a finite-time particle swarm optimization algorithm for odor source localization. First, a continuous-time finite-time particle swarm optimization (FPSO) algorithm is developed based on the continuous-time model of the particle swarm optimization (PSO) algorithm. Since the introduction of a nonlinear damping item, the proposed continuous-time FPSO algorithm can converge over a finite-time interval. Furthermore, in order to enhance its exploration capability, a tuning parameter is introduced into the proposed continuous-time FPSO algorithm. The algorithm’s finite-time convergence is analyzed by using the Lyapunov approach. Second, the discrete-time FPSO algorithm is obtained by using a given dicretization scheme. The corresponding convergence conditionis derived by using a linear matrix inequality (LMI) approach. Finally, the features and performance of the proposed FPSO algorithm are illustrated by using two ill-posed functions and twenty-five benchmark functions, respectively. In numerical simulation results, the problem of odor source localization is presented to validate the effectiveness of the proposed FPSO algorithm.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

277

Start Page

111

End Page

140

Number of Pages

30

eISSN

1872-6291

ISSN

0020-0255

Location

USA

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Hangzhou Dianzi University; TBA Research Institute;

Era Eligible

Yes

Journal

Information sciences.