Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Alsuhli, Ghada; Fahmy, Yasmine; Khattab, Ahmed

doi:10.1049/iet-its.2019.0366

Cited by 10 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…JSP technology architecture is adopted in the system design to realize interactive services. is architecture can handle the application logic of resources, and the development is relatively simple [13]. e server adopts Tomcat 5.0, and the background database adopts SQL server to realize data query and update data technology.…”

Section: Smart Classroom Teaching Platform Architecturementioning

confidence: 99%

Application of Internet of Things Architecture in Intelligent Classroom Teaching Analysis in Colleges and Universities

2022

Mobile Information Systems

View full text Add to dashboard Cite

Smart classroom is based on information technology, and its operation, upgrading, and development need technical support. At present, most of the smart classroom teaching adopts traditional architecture, highlighting the characteristics of disciplines, insufficient data processing, and narrow application fields. Based on this, this paper studies and analyzes the application of Internet of things architecture in college smart classroom teaching analysis. Based on the simple analysis of the development of intelligent algorithms and smart classrooms of the Internet of things, this paper constructs a smart classroom system based on the architecture of the Internet of things, transfers it to information-based teaching, introduces an intelligent service platform, and uses intelligent algorithms to mine and cluster data. The core algorithm is improved based on K-means algorithm and grid algorithm to improve the pertinence and effectiveness of the algorithm. Through the simulation results, the improved clustering analysis algorithm can shorten the running time and improve the clustering effect. At the same time, the application test results of the algorithm also show the superiority of the clustering results, which can provide more strategies for later teaching.

show abstract

Section: Smart Classroom Teaching Platform Architecturementioning

confidence: 99%

Application of Internet of Things Architecture in Intelligent Classroom Teaching Analysis in Colleges and Universities

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…The compared values prove that ESPEA outperformed other comparative optimizers in the most test cases. Indeed, the IGD findings demonstrate its efficient diversity and its reasonable convergence [18] after resolving test problems limited to 2 and 3 objectives.…”

Section: Related Workmentioning

confidence: 84%

An improved multi-objective Grey Wolf Optimizer

Tlili

MNASRI

Val

2022

Preprint

View full text Add to dashboard Cite

The existing multi-objective optimizers are not so efficient in resolving optimization problems with large number of objectives. For instance, we can mention the multi-objective Grey Wolf Optimizer (MOGWO) used to resolve many engineering problems. Despite its importance, the MOGWO performance is lower, compared to that of other recently-developed optimizers. Thus, this paper presents an Improved Multi-objective Grey Wolf Optimizer (IMOGWO) that allows solving more efficiently multi-objective optimization problems with a large number of objectives while achieving balance between convergence and diversity. The updates in MOGWO enhances the convergence and the exploration of the optimizer. The introduced algorithm was tested on a set of benchmark problems with various numbers of objectives (25 test cases). It was also compared with MOGWO and a set of recent and widely-employed optimizers. The experimental results show that IMOGWO outperformed MOGWO, on all studied benchmark problems, and other comparative algorithms in most test cases. Inferential statistical procedures were applied to highlight the superiority of IMOGWO over other algorithms.

show abstract

“…Alsuhli et al 27 proposed a many‐objective optimization algorithms‐based clustering mechanism (MOACM) which minimized the energy utilization and communication overhead in VANETs. This MOACM approach was proposed as a unified framework that integrated NSGA‐III, MOEA/DD, and ESPEA for determining the quality indicators that helped in determining the meta‐heuristics which is inherent with the best solutions' quality.…”

Section: Related Workmentioning

confidence: 99%

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

Nithyanandam,

Ambiyaram,

Prabathkumar

2023

Int J Communication

View full text Add to dashboard Cite

SummaryVehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)‐based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization‐based Node Clustering (HPDBWOA‐NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA‐NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA‐NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs.

show abstract

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Cited by 10 publications

References 51 publications

Application of Internet of Things Architecture in Intelligent Classroom Teaching Analysis in Colleges and Universities

Application of Internet of Things Architecture in Intelligent Classroom Teaching Analysis in Colleges and Universities

An improved multi-objective Grey Wolf Optimizer

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

Contact Info

Product

Resources

About