渐近式Bloch球面搜索的量子遗传算法及其应用

为了进一步加快搜索速度,提高优化效果,提出了一种渐近式Bloch球面搜索的量子遗传算法.在该算法中,首先采用Bloch球面坐标对量子染色体进行编码,然后基于最小二乘法理论,构建了量子染色体的更新策略,建立了量子旋转门角度大小和方向的公式,最后构造了变异操作中相位公式.将本文算法应用于多变量函数极值优化问题进行验证.实验结果表明,该算法不仅具有较好的种群多样性和随机性,而且还具有进化代数少、收敛速度快和优化效率高等优点.

A Bloch sphere quantum genetic algorithm based on gradual asymptotic search and its application

In order to quicken search speed and improve optimization effect, a Bloch sphere quantum genetic algorithm based on gradual asymptotic search was proposed. In this algorithm, quantum chromosomes were coded by Bloch sphere coordinates bits. Based on least square method, a new update strategy for quantum chromosomes was constructed. A formula, which contained the magnitude and direction of angle in quantum rotation gate, was established. The phase formula in mutation operation was configured. This proposed algorithm was applied in optimization of extreme value in multi-variables function to verify the performance. The experimental results showed that, the proposed algorithm not only has better diversity and randomicity of population, but also has the advantages of less iteration, faster convergence speed and better optimal efficiency.