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Object recognition using enhanced particle swarm optimization.

Willis, Michael; Zhang, Li; Liu, Han; Xie, Hailun; Mistry, Kamlesh


Michael Willis

Li Zhang

Han Liu

Hailun Xie

Kamlesh Mistry


The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation and sampling iteration operations for feature selection in object recognition. Specifically, five PSO variants integrating different mutation and sampling strategies have been proposed to select the most discriminative feature subsets for the classification of different objects. A mutation strategy is firstly proposed by randomly flipping the particle positions in some dimensions to generate new feature interactions. Moreover, instead of embarking the position updating evolution in PSO, the proposed PSO variants generate offspring solutions through a sampling mechanism during the initial search process. Two offspring generation sampling schemes are investigated, i.e. the employment of the personal and global best solutions obtained using the mutation mechanism, respectively, as the starting positions for the subsequent search process. Subsequently, several machine learning algorithms are used in conjunction with the proposed PSO variants to perform object classification. As evidenced by the empirical results, the proposed PSO variants outperform the original PSO algorithm, significantly, for feature optimization.


WILLIS, M., ZHANG, L., LIU, H., XIE, H. and MISTRY, L. 2020. Object recognition using enhanced particle swarm optimization. In Proceedings of 2020 International conference machine learning and cybernetics (ICMLC 2020), 4 December 2020, [virtual conference]. Piscataway: IEEE [online], pages 241-246. Available from:

Conference Name 2020 International conference on machine learning and cybernetics (ICMLC 2020)
Conference Location [virtual conference]
Start Date Dec 4, 2020
Acceptance Date Nov 15, 2020
Online Publication Date Dec 2, 2020
Publication Date Jul 5, 2021
Deposit Date Jul 27, 2021
Publicly Available Date Jul 27, 2021
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Pages 241-246
Series ISSN 2160-1348
ISBN 9780738124261
Keywords Feature selection; Object recognition; Optimization
Public URL


WILLIS 2020 Object recognition (AAM) (411 Kb)

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