基于文化基因算法的梯级电站负荷分配研究

Optimal Load Distribution of Cascade Hydropower Stations Based on Memetic Algorithm

为了解决传统基于种群进化的搜索算法求解电站负荷分配中搜索精度低、易陷入局部最优的问题,结合文化基因算法的框架,以粒子群算法(PSO)作为全局搜索策略,分别引入爬山算法(HP)与模拟退火算法(SA)作为局部搜索策略,形成HPMA、SPMA两种文化基因算法。设计了相应的局部搜索激活机制,并针对负荷分配问题初始可行解生成效率低的问题提出了一种初始种群快速生成方法。实例计算表明,两种文化基因算法较单独使用SA、PSO等算法具有更好的求解精度,同时SPMA算法优于HPMA算法,SPMA算法有利于提高了梯级水电站负荷分配问题的求解质量。

In order to solve the problem of low search accuracy and easy to fall into local optimum in traditional population evolution based on search algorithm for power station load distribution,after introducing the particle swarm optimization as global search strategy,and the hill-climbing algorithm and the simulated annealing algorithm as local search strategies respectively,two memetic algorithms of HPMA and SPMA are constructed based on the framework of memetic algorithm.Corresponding local search activation mechanisms are designed,and a fast initial population generation method is proposed to solve the problem of low initial feasible solution generation efficiency.The example calculation shows that two memetic algorithms have better accuracy than the algorithms using SA and PSO alone,and the SPMA algorithm is better than the HPMA algorithm.The SPMA algorithm is helpful to improve the quality of solving the load distribution problem of cascade hydropower stations.