Communication Resource Allocation Algorithm Based on Improved MOEA/D in Internet of Vehicles

To achieve an optimal solution for communication resource allocation in Internet of Vehicles（IoV），this study proposes a communication resource allocation optimization algorithm based on improved MOEA/D in IoV.The two conflicting network communication resource allocation elements：the blocking rate of...

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Bibliographic Details
Published in:Ji suan ji gong cheng Vol. 49; no. 5; pp. 191 - 197
Main Author: ZHENG Liping, ZHAO Yujuan, FEI Xuan
Format: Journal Article
Language:Chinese
English
Published: Editorial Office of Computer Engineering 01.05.2023
Subjects:
internet of vehicles（iov）|communication resource allocation|multi-objective evolutionary algorithm（moea）|moea/d algorithm|blocking probability|cost
ISSN:1000-3428
Online Access:Get full text
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Summary:To achieve an optimal solution for communication resource allocation in Internet of Vehicles（IoV），this study proposes a communication resource allocation optimization algorithm based on improved MOEA/D in IoV.The two conflicting network communication resource allocation elements：the blocking rate of resource requests in the vehicle network and the total cost of successful resource requests are considered as the two optimization objectives of network communication resource allocation.According to the characteristics of vehicles running in IoV，the constraint conditions are set for the vehicles requesting and providing resources.Accordingly，the adaptive neighborhood strategy is adopted to balance the convergence and distribution of the population in the evolution process，the iteration number is introduced into the adaptive degree，and the crossover operator and mutation operator are adjusted to ensure poor individuals also have heredity.Simultaneously，as the number of iterations increases，the heritability of poor individuals decreases，and that of good individuals increases，thereby ensuring the optimization of the population.The simulation results show that the algorithm can achieve satisfactory optimization results for both objectives.The optimal solution exists under different iteration times，number of vehicles，and number of resource requests.Under the same iteration times，the algorithm has lower blocking probability and better convergence compared with the dominated multi-objective SPEA2 and NSGA-II algorithms.
ISSN:1000-3428
DOI:10.19678/j.issn.1000-3428.0065747