Abstract: Aim to balance the problem of global optimal and local optimal in multi-modal function, an improved quantum genetic algorithm with immune operator is introduced. It carries both the quality of celerity of common quantum genetic algorithm and the quality of global searching of immune clone algorithm. It not only overcomes the flaw of the common quantum genetic algorithm which relapses into local optimum result but also avoids the flaw of the common immune clone algorithm which computes slowly. With the experiment of the global optimization of the multimodal function, the result indicates that this algorithm can settle the problem of searching the global optimization result in given range with faster speed and better result ,and it also shows us that it gets more robust stability compared to the common genetic algorithm and the common quantum genetic algorithm.

Key words: quantum genetic algorithm, immune algorithm, multi-modal function, global optimization

摘要： 针对多峰函数优化中的全局及局部寻优问题，提出了一种结合免疫克隆算子的量子遗传算法，给出了实现流程。该算法集量子遗传算法的快速性和免疫克隆算法全局搜索性于一身。它不仅有效克服了量子遗传算法容易陷于局部最优的缺点，也避免了普通免疫克隆算法计算缓慢的缺点。用多峰值函数进行了全局寻优的仿真实验，并与基本遗传算法，量子遗传算法的计算结果进行了比较，结果表明所提算法能以较快的速度搜索到全局最优解，并且其鲁棒性远高于普通量子遗传算法和遗传算法。

关键词: 量子遗传算法, 免疫算法, 多峰值函数, 全局优化