Multi-objective Optimization of Cross-Layer Configuration for Cognitive Wireless Network

Resources optimization of cross-layer is a typical multi-objective optimization problem. In this paper, an adaptive clone and neighbor selection algorithm is proposed to resolve optimization resources allocation in cognitive radio networks (CRNs). The algorithm uses the adaptive cloning operator, neighborhood search operator to improve the performance of algorithm. Simulation comparisons for typical test functions show that the proposed algorithm can effectively solve the multi-objective optimization. We use the proposed algorithm to optimize two goals of used bandwidth and power consumption for cognitive networks. And then the fuzzy algorithm is used to select the best configuration parameters of bandwidth and power from a set of optimal solutions. Simulation results show that the algorithm can effectively address the spectrum allocation and power control in cognitive radio networks.