Improved decomposition-based multi-objective cuckoo search algorithm for spectrum allocation in cognitive vehicular network

Abstract

The allocation of spectrum resources efficiently and equitably in dynamic cognitive vehicular networks is more challenging than static cognitive networks. Currently, most spectrum allocation algorithms are on the basis of a fixed network topology, thereby ignoring the mobility of cognitive vehicular users (CVUs), timeliness of licensed channels, and uncertainty of spectrum sensing in complex environments. In this paper, a cognitive vehicular network spectrum allocation model for maximizing the network throughput and fairness is established considering these factors. A rapid convergence, improved performance algorithm for solving this multi-objective problem is necessary to adapt to a dynamic network environment. Therefore, an improved decomposition-based multi-objective cuckoo search (MOICS/D) algorithm is proposed. This algorithm integrates a decomposition-based multi-objective optimization framework and an improved CS algorithm. The multi-objective problem is decomposed into multiple scalar sub-problems with different weight coefficients, and the cuckoo algorithm with adaptive steps is used to optimize these sub-problems simultaneously. Simulation results show that the MOICS/D algorithm has faster and more stable convergence than the MOEA/D and NSGA-II algorithms and can improve the throughput and fairness of the network.