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根据感知的频谱环境变化及时优化并调整无线电参数是认知无线电的关键技术之一,也是一个复杂的非线性多目标优化决策问题。遗传算法是最适合优化问题的,但当遗传算法应用于优化问题时存在过早收敛问题。提出了基于遗传算法和人工免疫系统相结合的免疫遗传算法(IGA)来克服以上问题。由于在GA算法中引入了免疫系统中抗体和抗原的概念并在每一次迭代中丢弃亲和力较大的抗体,有效地防止了GA中过早收敛现象。最后,用免疫遗传算法来解决认知无线电的参数优化问题。仿真结果表明,免疫遗传算法可以迅速达到最优决策。
It is one of the key technologies of cognitive radio to optimize and adjust the radio parameters timely according to the changes of the perceived spectrum environment. It is also a complex nonlinear multi-objective optimization decision-making problem. Genetic algorithm is the most suitable for optimization problems, but premature convergence problems exist when genetic algorithms are applied to optimization problems. An immune genetic algorithm (IGA) based on genetic algorithm and artificial immune system is proposed to overcome the above problems. The premature convergence in GA is effectively prevented by introducing the concept of antibodies and antigens in the immune system into the GA algorithm and discarding the more avid antibodies at each iteration. Finally, the immune genetic algorithm is used to solve the parameter optimization problem of cognitive radio. Simulation results show that the immune genetic algorithm can quickly reach the optimal decision.