基于NW小世界的量子进化算法在无功优化中的研究

针对量子进化算法的早熟问题，提出了一种适于电力系统无功优化的NW（newman-watts）小世界量子进化算法。该算法引入了NW小世界网络模型，以一种新颖的随机加边方式动态改变种群个体的邻域拓扑结构，从而保证了整个优化过程中的种群多样性，提高了算法的全局搜索能力。应用该算法对IEEE-14节点和IEEE-57节点系统进行无功优化的仿真分析，结果表明，NW小世界量子进化算法在电网无功优化计算中具有较强的全局寻优能力和较高的收敛精度。

Research on Reactive Power Optimization Using Quantum Evolutionary Algorithm Based on NW Small World Model

In view of the premature convergence problem of quantum evolutionary algorithm(QEA), a novel NW(Newman-Watts) small- world quantum evolutionary algorithm is proposed for reactive power optimization. This algorithm introduces the NW small world network model, and dynamically changes the neighborhood topology of each individual among population through a novel random adding edge method, which not only guarantees the diversity of evolution population, but also improves its balance capability of global exploration. Finally, the proposed algorithm is applied to address the reactive power optimization problem in IEEE 14-bus system and IEEE 30-bus system. The simulation analysis shows that the proposed NW small-world quantum evolutionary algorithm outperforms the standard QEA both in global search capability and convergence precision.