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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.
In order to further accelerate the search speed and improve the optimization effect, a quantum genetic algorithm for asymptotic Bloch spherical search is proposed, in which the quantum chromosomes are first encoded using Bloch spherical coordinates and then based on the least squares theory, Quantum chromosome, the formula of the size and direction of the quantum revolving door angle is established, and finally the phase formula in the variation operation is constructed.The algorithm is applied to the extremal optimization problem of multivariable function.Experimental results show that the algorithm not only has Good population diversity and randomness, but also has the advantages of less evolutionary algebra, faster convergence rate and higher efficiency.