采用多局部搜索策略的无功优化多模因算法

引入局部搜索策略可提高无功优化进化算法的收敛性能，但目前引入的局部搜索策略都比较单一，不能取得很好的收敛效果。因此该文提出一种基于多局部搜索策略的无功优化多模因算法。该算法根据现有的多种局部搜索策略，提出包含修正型、定向型和随机型3类模因的无功优化模因池。在IEEE 30节点标准系统上的仿真表明，新算法可发挥各类局部搜索策略的特点，具有良好的收敛特性。此外，该文还比较不同作用比例的多模因算法，分析作用比例与算法效率的关系，为局部搜索策略在无功优化中的进一步应用提供参考。

Multimeme Memetic Algorithm Based on Multiple Local Search Strategies for Reactive Power Optimization

Local search strategies (LSSs) are commonly introduced to promote the convergence of evolutionary algorithm for reactive power optimization (RPO). Existing algorithms for RPO, however, adopt single LSS or several LSSs with similar search characteristics and can not obtain remarkable performance in convergence. In this paper, a new algorithm based on multiple LSSs, multimeme memetic algorithm (MMA) is proposed. Meme pool containing correcting memes, directed memes and stochastic memes, three memes complementing each other, is presented. The algorithm is applied in IEEE 30 bus system and numerical simulations demonstrate that the algorithm combines all the advantages of LSSs and shows the best performance of convergence. In addition, simulation results with different percentage of population involved in LSSs are compared and relationship between percentage of population and efficiency of the algorithm is analyzed.