一种量子行为进化算法及应用

为了提高进化算法的优化能力,提出一种量子行为进化算法.该算法基于Bloch球面建立搜索机制,首先用量子位描述个体,用泡利矩阵建立旋转轴,用量子位在Bloch球面上的绕轴旋转实现进化搜索；然后用Hadamard门实现个体变异,以避免早熟收敛.这种旋转可使当前量子位沿着Bloch球面上的大圆逼近目标量子位,从而可加速优化进程.以函数极值优化为例,实验结果表明该算法具有较高的优化能力和优化效率.

A quantum-behaved evolutionary algorithm with applications

In order to improve the ability of the optimization of the evolutionary algorithm, a quantum-behaved evolutionary
algorithm is proposed. In this algorithm, the search mechanism is built based on the Bloch sphere. Firstly, the individuals are expressed with qubits, the axis of revolution is established with Pauli matrix, and the evolution search is realized with the rotation of qubits in the Bloch sphere. Then, in order to avoid premature convergence, the mutation of individuals is achieved with Hadamard gates. Such rotation can make the current qubit approximate the target qubit along with the biggest circle on the Bloch sphere, which can accelerate the optimization process. Taking the function extreme value optimization as an example, the experimental results show that the proposed algorithm has higher optimization ability and optimization efficiency.