链式双链量子遗传算法

Double Chains Quantum Genetic Algorithm

针对双链量子遗传算法具有收敛速度慢,容易陷入局部最优解等问题,提出一种新的双链量子遗传算法。该算法将种群个体分组,相邻组间有一个共有个体,即第i组的最后一条染色体同时是第i+1组的第一个个体。组内各染色体同方向同步长更新,相邻组间通过共有个体保持组间同步。该方法能很好地降低算法时间复杂度,保持种群个体的多样性,从而避免算法陷入局部最优值。最后通过实验验证该算法对复杂函数的优化结果明显优于双链量子遗传算法。

Aiming at the problem that the double chains quantum genetic algorithm has low convergence rate and is easy to fall into local optimum value, a new quantum genetic algorithm is proposed. In this algorithm, the population is divided into several groups, and there exists a common member between the two neighboring groups. For example, the last member of the ithgroup is also the first member of the（i＋1）thgroup. All the members of the same group are updated in the same direction with the same step.At the same time, through the common member, the neighboring groups keep pace with one another. This method can reduce the time complexity, maintain the population diversity, and avoid making the algorithm fall into the local optimum value. The simulation results show that the algorithm is much more efficient in the optimization of complex functions than double chains quantum genetic algorithm.