基于改进人工蜂群算法的认知抗干扰智能决策技术研究
Cognitive Anti-jamming Intelligent Decision Based on Improved Artificial Bee Colony Algorithm
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摘要: 在认知抗干扰系统中,智能决策是其核心,根据干扰环境,对系统的干扰抑制方式、频谱资源分配、调制编码方式和功率调整信息进行最优决策。人工蜂群算法(Artificial Bee Colony,ABC)相较于其他群体智能算法全局寻优速度更快,设置参数少、灵活,易与其他技术结合改进原算法,实用性更广泛,但ABC算法同样有其局限性,如局部搜索能力较弱、后期收敛速度慢等。针对复杂干扰环境下对离散参数的决策,本文设计了一种基于改进人工蜂群算法的认知抗干扰智能决策引擎,分析了引擎模型,根据系统效能设计了目标函数和染色体,阐述了决策实现步骤,优化了决策参数,提出了按基因组搜索的改进算法;通过对系统抗干扰性能的仿真,验证了与未采用智能决策的抗干扰系统相比,采用本文提出的智能决策引擎的认知抗干扰系统在干扰环境中不仅具有强抗干扰性能,而且在保证通信传输可靠性的前提下,具有较低的发射功率和高传输效率,与采用传统人工蜂群算法和遗传算法的决策引擎相比,基于改进人工蜂群算法的决策引擎平均收敛代数更少且最优解概率更高。
Abstract: Intelligent decision-making is the core of the cognitive anti-jamming system, the optimal decision is made on the system’s jamming suppression mode, spectrum resource allocation, modulation and coding mode and power adjustment information according to the jamming environment. Compared with other swarm intelligence algorithms, Artificial Bee Colony(ABC) has faster global optimization, less parameter setting and more flexibility, and it is easy to combine with other technologies to improve the original algorithm. It is more practical, but ABC algorithm also has its limitations, such as weak local search ability, slow convergence in the later period. For the decision of discrete parameters in complex interference environments, this paper designs an intelligent anti-interference intelligence decision engine based on improved artificial bee colony algorithm, analyzes the engine model, designs the objective function and chromosome based on the system efficiency, elaborates the decision-making steps, optimizes the decision-making parameters and proposes an improved algorithm based on genome search. Through the system anti-jamming performance simulation, it is verified that compared with the anti-jamming system without intelligent decision engine, the cognitive anti-jamming system using the intelligent decision engine proposed in this paper not only has strong anti-jamming performance in the interference environment, but also has lower transmission power and higher transmission efficiency on the premise of guaranteeing the reliability of communication transmission. Compared with decision engine using original artificial bee colony algorithms and genetic algorithm, the decision engine based on improved artificial bee colony algorithm has fewer average convergence algebras and higher optimal solution probability.