基于量子的免疫进化算法及收敛性

分析和探讨了量子计算的特点及免疫进化机制,并结合免疫系统的动力学模型和免疫细胞在自我进化中的亲和度成熟机理,提出了一种基于量子计算的免疫进化算法。该算法使用量子比特表达染色体,通过免疫克隆、记忆细胞产生和抗体相似性抑制等进化机制可最终找出最优解,它比传统的量子进化算法具有更好的种群多样性、更快的收敛速度和全局寻优能力。在此不仅从理论上证明了该算法的收敛,而且通过仿真实验表明了该算法的优越性。

Immune Evolutionary Algorithm Based on Quantum Computing and Its Convergence

The characteristics of quantum computing and the mechanism of immune evolution are analyzed and discussed.Inspired by the mechanism in which immune cell can gradually accomplish affinity maturation during the self-evolution process,a immune evolutionary algorithm based on quantum computing (MQEA) is proposed.The algorithm can find out optimal solution by the mechanism in which antibody can be clone selected,memory cells can be produced,similar antibodies can be suppressed and immune cell can be expressed as quantum bit (Q-bit).It not only can maintain quite nicely the population diversity than the classical evolutionary algorithm,but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly.The convergence of the MQEA is proved and its superiority is shown by some simulation experiments in this paper.