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A finite-time particle swarm optimization algorithm for odor source localization
journal contributionposted on 2017-12-06, 00:00 authored by Qiang LuQiang Lu, Qing-Long HanQing-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.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages30
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External Author AffiliationsCentre for Intelligent and Networked Systems (CINS); Hangzhou Dianzi University; TBA Research Institute;