ACBSO: a hybrid solution for load balancing using ant colony and bird swarm optimization algorithms

In order to balance the load in cloud computing, this study suggests a hybrid technique called "ACBSO" that uses both ant-colony and bird swarm optimization techniques. Comparative analysis of the proposed hybrid algorithm has been done with Ant-colony optimization and bird swarm optimizat...

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Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 15; H. 5; S. 2847 - 2857
Hauptverfasser: Raghav, Yogita Yashveer, Vyas, Vaibhav
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2023
Springer Nature B.V
Schlagworte:
Algorithms
Ant colony optimization
Artificial Intelligence
Birds
Cloud computing
Computer Imaging
Computer Science
Concurrency control
Image Processing and Computer Vision
Literature reviews
Machine Learning
Optimization algorithms
Optimization techniques
Original Research
Pattern Recognition and Graphics
Scheduling
Software Engineering
Vision
Workloads
Cloud computing
Ant colony optimization
Bird swarm optimization
Swarm intelligence
Load balancing
ISSN:2511-2104, 2511-2112
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Zusammenfassung:In order to balance the load in cloud computing, this study suggests a hybrid technique called "ACBSO" that uses both ant-colony and bird swarm optimization techniques. Comparative analysis of the proposed hybrid algorithm has been done with Ant-colony optimization and bird swarm optimization techniques. Simulation results show that the hybrid approach outperforms both the ant-colony optimization approach and the bird swarm optimization approach. Comparison has been done based on primary factors such as makespan, throughput, fitness score, and resource consumption. To obtain the outcomes of the necessary simulations, the cloudsim simulator is employed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01340-5