混合编码粒子ABC算法的CR认知决策引擎

Cognitive radio decision engine based on hybrid encoding particle ABC optimization algorithm

认知决策引擎是认知无线电(Cognitive Radio,CR)的核心。为适应CR参数的自适应重配置,提出了一种改进的二进制人工蜂群(Binary Artificial Bee Colony algorithm,BABC)算法。该算法在基本BABC算法的基础上,加入了反向学习初始化机制、混合编码规则以及社会认知策略,保证了个体的多样性、提高了搜索速度。给出了该算法的基本步骤,并在多载波通信系统中对算法性能进行了仿真。仿真结果表明,基于该算法的CR认知决策引擎的收敛速度和精度均优于经典的遗传算法(Genetic Algorithm,GA)和BABC算法,优化得到的系统参数具有更好的性能。

The core of the Cognitive Radio（CR）is decision engine. In order to adapt reconfiguration of the CR parameters, an improved Binary Artificial Bee Colony（BABC） algorithm is proposed. The algorithm is based on the basic BABC algorithm, and the opposition-based learning initialization mechanism, hybrid coding rules, and social cognitive strategies are added to ensure the diversity of individuals to improve the search speed. The key steps of the proposed Hybrid Encoding Particle Bee Colony（HABC）optimization algorithm are presented and multicarrier system is used for simulation analysis. The experimental results show that the convergence speed and accuracy of the proposed method is superior to the classical Genetic Algorithm（GA）and BABC method, CR decision engine optimization parameters have better performance.