基于改进免疫遗传算法的水电站优化调度应用研究

Research on application of optimal operation of hydropower station based on improved immune genetic algorithm

根据免疫算法的生物学机理,提出了一种改进的免疫遗传算法.该算法将微粒群算法作为免疫算法的全局搜索策略,提高算法的全局搜索能力;利用逐步优化算法对免疫算法的控制策略进行进化操作,提高算法的局部搜索能力;利用免疫算法本身基于浓度的自我调节机制,提高群体的多样性,避免算法过早陷入局部最优解.最后给出了该算法实现的具体步骤,并将其应用于水电站的优化调度中,取得了较为满意的结果,且与动态规划、遗传算法、免疫算法和微粒群算法等比较,验证了算法的有效性和优越性.

Based on the biological mechanism of immune algorithm, an improved immune geneuc algorithm is proposed, in which particle swarm optimization is taken as global searching strategy to improve the global search ability of the immune genetic algorithm, and progressive optimization algorithm is used for evolving operation of control strategy to improve its local search ability. At the same time, because of self-regulatory mechanism of immune algorithm based on concentration, group diversity can be improved and algorithm can be avoided to fall into local optimal solution too early. The specific realization process of the algorithm is presented, as well as the algorithm is applied to optimal operation of a hydropower station, which indicates that the results are satisfied, including the effectiveness and superiority compared with dynamic programming, genetic algorithm, immune algorithm and particle swarm optimization.