Parameter optimization of cognitive engine based on chaos multi-objective immune algorithm

Reasonable setting of engine parameters can improve the performance of the spectrum use. By analyzing engine parameter adjustment of cognitive wireless network, the mathematical model is given, and then it is converted into a multi-objective optimization problem. A chaos multi-objective immune algorithm is proposed to solve the problem. Logistic mapping is used to initialize population and search for the best solutions in every generation. The operators of cloning and antibodies updating are designed, which ensures that the distribution of Pateto optimal solutions is more diverse and uniform. Finally, the simulation experiments are done to test the algorithm under multi-carrier system. The results show that the algorithm can adjust transmission power and modulation mode according to the change of channel and user demands. More solutions with preferences are obtained, so it meets the demands for parameter optimization of cognitive engine.