Multi-Objective Emperor Penguin Optimizer for Tuning the Quality-of-Service in Cloud Computing

Kaur, Harinder; Bhadoria, Robin Singh

doi:10.1109/csnt51715.2021.9509727

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The emperor penguins colony is an inspired meta-heuristic algorithm with fewer errors and better performance. A multi-objective emperor penguin optimizer is employed to alter the QoS [24]. This method uses the QoS parameter, particularly on throughput, and applies cloud rank 3 with the help of the multi-objective penguin optimizer algorithm.…”

Section: Introductionmentioning

confidence: 99%

Penguin search with Harris-Hawk optimization algorithm to improve clustering performance in wireless network

Sabapathy Ranganathan,

Sampathrajan

2024

IJEECS

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<span>Integrating optimal search algorithm concepts across the wireless core and cluster structure enables next-generation wireless networks to effectively provide reliable low-delay communications and connectivity for internet of things (IoT) devices. This article describes penguin search with the Harris-Hawk optimization algorithm (PHHO) to improve clustering performance in wireless networks. The penguin search optimization algorithm (PSO) algorithm computes the fitness value for feature selection from the database. Harris-Hawk optimization (HHO) algorithm to reduce the time and energy required for network transmission. This mechanism builds the clusters based on node communication range. The node direction, node mobility, node bandwidth availability, and energy parameters to decide the cluster head (CH) by applying the HHO algorithm. This approach uses a PSO algorithm fitness function to select the feature subset to minimize error and overhead in the network. Using a network simulator (NS)-3, this method assesses and chooses the most efficient way for data transmission, and the result is compared to a baseline mechanism.</span>

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Section: Introductionmentioning

confidence: 99%