关键词: 量子遗传算法, 免疫算法, 多峰值函数, 全局优化

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