Bacterial foraging optimization with double role of reproduction and step adaptation

Daas, Mohamed Skander; Chikhi, Salim; Batouche, Mohamed

doi:10.1145/2816839.2816868

Cited by 4 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar approach of adaptation along with enhancement in reproduction step is carried out by Daas et al [20]. BFO algorithm was also reported to be used for optimizing the traffic performance under the congestion state as seen in work of Jain et al [21]. Usage of BFO was reported to be used for retaining uniform output of power in grid system as seen in the work of Mishra et al [22].…”

Section: Introductionmentioning

confidence: 93%

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

View full text Add to dashboard Cite

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance.

show abstract

Section: Introductionmentioning

confidence: 93%

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

View full text Add to dashboard Cite

show abstract

“…However, classifier training time might also be increased. Daas, Chikhi, and Batouche (2015) proposed the FABFO algorithm which eliminated dispersal and reproduction steps found in the BFO algorithm. Such an approach increased convergence speed and reduced time complexity.…”

Section: Related Workmentioning

confidence: 99%

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

Karthikeyan

Mohmed

Ruhaiyem

2019

JICT

View full text Add to dashboard Cite

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.

show abstract

Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm

Shanmugasundaram

Mohamed

Ruhaiyem

2017

Advances in Visual Informatics

View full text Add to dashboard Cite

Bacterial foraging optimization with double role of reproduction and step adaptation

Cited by 4 publications

References 13 publications

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

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

Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm

Contact Info

Product

Resources

About