Optimal Design of Cognitive Radio Wireless Parameters based on Non-dominated Neighbor Distribution Genetic Algorithm

Cognitive radio (CR) wireless parameter optimization is a typical multi-objective optimization problem. In order to optimize wireless parameters, a non-dominated neighbor distribution multi-objective genetic algorithm (NNDA) is presented in this paper. Based on non-dominated sorting, NNDA increases the probability that superior individuals pass down to next generation by crowding distance and distribution mechanism. Comparative studies are performed between the NNDA and NNIA by typical test functions. Simulation results show that NNDA can effectively solve the multi-objective optimization problems and has a more fast convergence and reasonable result than NNIA. By applying the NNDA to the optimization problem of CR, the simulation results show that the algorithm is effective in the optimization of cognitive radio parameters design.