自适应相位旋转的量子菌群算法

Quantum Bacterial Foraging Algorithm with Adaptive Phase Rotation

量子菌群算法是将量子计算与菌群觅食优化算法相融合而得到的一种量子智能算法,但该算法存在鲁棒性比较差和寻优时间比较长的缺陷。为解决该问题,本文设计了一种旋转相位自适应调整的量子旋转门,并用其完成细菌的趋化操作,提出了一种自适应相位旋转的量子菌群算法。通过16个不同类型的标准测试函数对其优化性能进行研究,统计结果表明该算法在低维时,对于多种种类的测试函数,在收敛精度和稳定性上都要优于改进前的量子菌群算法,且优化结果要明显优于经典的菌群觅食优化算法和量子遗传算法。进一步研究表明,在达到指定收敛精度的情况下,该算法的平均收敛概率是最高的,平均运行时间和平均迭代步数是最短的。而在高维情况下,该算法则对碗状和碟状类型的测试函数比较适用。

Quantum bacterial foraging optimization algorithm is an quantum intelligence algorithm which is based on the concept of quantum computing and bacteria foraging optimization algorithm. However, this algorithm exists the defects of poor robustness and the problem of long running time in optimization. To solve these problems, this paper designs a quan- tum rotation gate which has a adaptive phase rotation. Using this rotation gate simulating the bacterial chemotaxis operation, this paper proposes a quantum foraging algorithm based on adaptive phase rotation. To test the new algorithm＇ s optimization performance, a research based on sixteen benchmark functions is conducted. The results indicate that in the situation of low dimension, the new AQBFO algorithm shows better results than QBFO in convergence precision and stability, say nothing of QGA and BFO. Further research shows that, average convergence probability of the proposed algorithm is the highest and the average running time and average running steps are the shortest among the four algorithms when reach the specified con- vergence precision. While in the situation of high dimension, this algorithm is suitable for bowl shape and plate shape benchmark functions.