自主迁移的并行遗传算法用于马斯京根模型参数估计

Parallel Genetic Algorithm with Self-migration to Parameter Estimation of Muskingum Model

遗传算法是一种随机性的全局优化算法,但简单遗传算法易陷入局部最优。将并行技术与遗传算法相结合,且针对影响并行遗传算法性能的迁移时机进行研究,提出自主迁移的并行遗传算法用于马斯京根模型参数估计。实验结果表明,该算法为估计马斯京根模型参数提供了一种有效的方法。

Cenetic dgorithm is one of randomg lobal optimization algorithms,but the simple genetic algorithm is easy to fall into local optimal. Unify the parallel technology and the genetic algorithm, also conducts the research in view of the influence parallel genetic algorithm performance migration opportunity, this paper proposed the parallel genetic algorithm with self-migration for estimating the parameter of Muskingum model. The experimental result indicated that, this algorithm for estimated the parameter of Muskingum model has provided one effective method.