Enhancing fault tolerance in vehicular ad-hoc networks using artificial bee colony algorithm-based spanning trees

In order to study the development of the sports economy in different regions and analyze the future development prospect of sports economy, this paper uses the k-clustering method to improve the artificial bee colony algorithm and further improve the clustering degree of the bee colony. Among them, the improved artificial bee colony algorithm reduces the incidence of local extreme and improves the accuracy of calculation by setting the weight and threshold of indicators. MATLAB simulation results show that the prediction accuracy of the improved artificial bee colony algorithm for the development prospect of sports economy is 96–99%, and the calculation time is 0–17 seconds. Therefore, the improved artificial bee colony algorithm can best predict the development of the sports economy in different regions, and its accuracy, periodicity, and calculation time are better than those of the original artificial bee colony algorithm.

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Enhancing fault tolerance in vehicular ad-hoc networks using artificial bee colony algorithm-based spanning trees

Cited by 3 publications

References 60 publications

Quantum Binary Improved Artificial Bee Colony Algorithm to Solve the Spanning Tree Construction Problem in Vehicular Ad Hoc Network

Quantum Binary Improved Artificial Bee Colony Algorithm to Solve the Spanning Tree Construction Problem in Vehicular Ad Hoc Network

Computation Offloading and Resource Allocation in Failure-Aware Vehicular Edge Computing

The Prediction of Sports Economic Development Prospect in Different Regions by Improved Artificial Bee Colony Algorithm

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