基于初始种群变异遗传算法的水电站优化调度

Optimal Scheduling of Hydropower Station Based on the Initial Population Variability Genetic Algorithms

为解决自适应遗传算法应用于水电站优化调度时易陷入局部最优解的问题,在自适应遗传算法中加入了初始群体变异策略,首先对初始种群进行深度变异并保存优秀个体,然后再对保存的优秀个体采用自适应遗传算法进行全局搜索。水电站优化调度实例表明:采用初始种群变异策略的遗传算法具有更高的全局搜索能力,得出的结果比自适应遗传算法更佳,克服了自适应遗传算法易过早陷入局部最优的缺陷。

In order to solve the adaptive genetic algorithm is easy to fall into local optimal solution when applied to hydropower station optimal scheduling, this paper presented a group to join the initial improvements in adaptive mutation strategy genetic algorithm. First, it made depth variation of the initial population and preserved outstanding individuals, ensuring the diversity of the initial population and then globally searched the saved outstanding individuals by adaptive genetic algorithm. Hydropower station optimal scheduling example shows that the mutation strategy using genetic algorithm initial population has higher global search capability, The result is better than that of the adaptive genetic algorithms and has avoided the adaptive genetic algorithm premature local optimum defects.