Biometric authentication of physical characteristics recognition using artificial neural network with PSO algorithm

Evangelin, L. Nisha; Fred, A. Lenin

doi:10.1504/ijcat.2017.088196

Cited by 6 publications

(1 citation statement)

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“…Particle swarm optimization (PSO) is one of the most important swarm intelligence paradigms [1]. Due to its simple structure, strong operability and easy to implement, PSO algorithm has many successful applications seen in solving real-world optimization problems, such as controller parameters tuning [2], rectangular packing problem [3], neural network optimization [4], large-scale social network clustering solution and are easy to get trapped in the local optimum, especially in the largescale complex optimization problem [7].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Center PSO Algorithm with Memory Ability and Its Application to the Online Modelling of an Underwater Vehicle Thruster

Wang

et al. 2019

Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019)

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To improve the performance of Particle swarm optimization (PSO) in online optimization problems, a multi-center PSO algorithm with memory ability was proposed. Main strategies of the proposed algorithm include the initial population optimization based on historical optimal solution and improved chaos mapping and the multi-center collaborative search. To verify online optimization performance, the proposed algorithm is applied to the online modelling process of an underwater vehicle thruster to optimize the modeling parameters. Result proves the superiority of the proposed algorithm in online optimization problem. Keywords Online optimization • PSO • Tent mapping • Multi-center collaborative search • Online modeling • Underwater vehicles

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