多群体阶段性杂交遗传算法

借鉴生物遗传学提出了一种多群体阶段性杂交遗传算法。引入相对顺序交叉算子对标准遗传算法进行了改进。为验证该算法的性能，对旅行商问题进行了求解，采用多群体和阶段性杂交的改进策略，并分别和标准遗传算法进行了比较。计算结果表明，该方法能较好地保证个体多样性，并能促进优秀基因型的杂交和遗传，在收敛和鲁棒性方面优于一般的单群体、非杂交算法。另外，将其应用于水电站优化调度也取得了较好的效果。

Stages Hybrid Genetic Algorithm with Multi-Group

Drawing on idea of genetics,a staged hybrid genetic algorithm (SHMGA) with multi-group (SHMGA) is proposed. Firstly, the relative sequential crossover operator is introduced into the standard genetic algorithm (GA) to improve its performance,then a SHMGA is designed to solve the traveling salesman problem for testifying its ability with the adoption of the multi-group and staged hybrid policies. Computational results show that this method can ensure the diversity of individuals and promote the crossbreed and heredity of good genetypes so it has better convergence and robustness compared with the standard genetic algorithm, i.e. single-group or non-hybrid GA. Additionally it is applied to the schedule optimization of hydropower stations and shown to be valid.