面向离散优化问题的量子协同演化算法

为解决现有离散优化算法在有限时间内容易出现过早收敛或难以收敛的问题，提出了面向离散优化问题的量子协同演化算法。该算法通过种群初始化策略构建分布均匀的初始种群，并改进粒子群和单点优化算法成为具有不同搜索能力的协同演化策略，进而利用量子旋转门根据种群个体的进化情况自适应地选择合适的演化策略，最后利用精英保持策略避免种群的退化。在标准离散问题和背包问题的测试环境中，各算法的平均收敛精度和实际收敛情况均表明，已提出的算法能够在有限时间内，收敛到精度较高的解，可用于求解具有时效要求的离散优化问题。

Quantum-Coevolutional Algorithm for Discrete Optimization Problem

In order to solve the problem that the existing optimization algorithm is prone to premature convergence or non-convergence in a finite time, a quantum-coevolutional algorithm (QCA) for discrete optimization problems was proposed. In this algorithm, the initial population was uniform distributed by the population initialization strategy, and the existing Particle Swarm Optimization algorithm and the Single Point algorithm were modified into the co-evolutionary strategies with various searching ability. Then, Quantum Rotation Gate was used to adaptively select the appropriate evolution strategy according to the individual evolution. Finally, the algorithm utilized elitist strategy to avoid the degradation of population. In the test environment of standard discrete problems and Knapsack Problem, the results of average convergence precision and the actual convergence showed that the proposed algorithm could converge to the solution of high accuracy in a finite time. Therefore, the proposed algorithm can be used to solve the discrete optimization problem with the solution time.