基于带疫苗注入VAMIGA的认知无线电波形优化

典型基于遗传算法的认知无线电(CR)引擎多采用加权法将多个优化目标转换为单目标进行处理,这容易漏掉最优解且引擎效率较低。针对该问题提出了一种带疫苗注入的自适应多目标免疫遗传算法(VAMIGA)。通过在CR问题中与强度Pareto进化算法(SPEA2)仿真对比,VAMIGA决策结果降低了2%~15%的发射功率,提高了6%~8%的调制指数,降低了6%~36%的误比特率。由此可见该算法能更有效地解决多目标优化和不同环境下的CR波形设计问题。

Optimization for Cognitive Radio Waveform Based on Adaptive Multi-Objective Immune Genetic Algorithm with Vaccine Injection

The current cognitive radio (CR) engine based on genetic algorithm usually adopts a weight-method to change multi-objective into a single objective, which may miss optimal solutions and reduce the efficiency of engine. This paper proposes an adaptive multi-objective immune genetic algorithm with vaccine injection (VAMIGA) to resolve this problem. The vaccine injection could optimize the decision result and convergence speed by saving and recycling the excellent genes. Compared with the strength Pareto evolutionary algorithm (SPEA2) on CR problems, the simulation results show that the VAMIGA reduces 2%~15% of the transmitted power and 6%~36% of the bit error rate (BER), and improves 6%~8% of modulation index. Thus, the VAMIGA can work more efficiently to solve multi-objective optimization and CR waveform design in different environment.