Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm

Shanmugasundaram, K.; Mohamed, Ahmad Sufril Azlan; Ruhaiyem, Nur Intan Raihana

doi:10.1007/978-3-319-70010-6_7

Cited by 1 publication

(2 citation statements)

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“…The methodology of the study consisted of two phases: i) implementation of Hybrid BFO-PSO (HIBS) algorithm (Shanmugasundaram et al, 2017a) and ii) deployment of Hybrid BFO-PSO (HIBS) algorithm in a hand-based multibiometric authentication system. The role of the hybrid algorithm was to select optimal weights at the score fusion which involved error minimization (EER) as the performance measure.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In this paper, the proposed algorithm is used to mitigate the weaknesses of BFO (Passino, 2002) and PSO (Eberhart & Kennedy, 1995) algorithms that include slow and premature convergence (Shanmugasundaram, Mohamed, & Ruhaiyem, 2017a). At the score fusion (Ross et al, 2006), the hand-based multimodal biometric traits like fingerprint, palm print and finger inner knuckle print are fused along with optimal weights induced by the hybrid algorithm which minimizes error rates.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Improved Bacterial Swarm Optimization Algorithm for Hand-Based Multimodal Biometric Authentication System

Karthikeyan

Mohmed

Ruhaiyem

2019

JICT

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This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%