A hybrid of particle swarm optimization and genetic algorithm for multicarrier Cognitive Radio

Cognitive radio (CR) has become a hotspot in recent research. We can think of a CR as having three main parts: the ability to sense, the capacity to learn, and the capability to adapt. Adaptation to the outside environment to optimize radio parameters has been previously proposed using genetic algorithms (GA) to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. However, the time required for the genetic algorithms to find a solution increases as the system complexity grows, as in multicarrier (MC) systems. In this paper, we propose a new faster algorithm based on a hybrid of binary-coded particle swarm optimization and genetic algorithm (HBPGA). The new algorithm is compared to GA, and the binary particle swarm optimization (BPSO), simulation results show that it performs better than both in terms of convergence speed and converged fitness value.