动态双子群协同进化果蝇优化算法

Dynamic Double Subgroups Cooperative Fruit Fly Optimization Algorithm

针对基本果蝇优化算法(FOA)寻优精度不高和易陷入局部最优的缺点,提出动态双子群协同进化果蝇优化算法(DDSCFOA).该算法在运行过程中根据群体的进化水平,动态地将整个种群划分为先进子群和后进子群;先进子群采用混沌算法在局部最优解邻域内进行精细的局部搜索,后进子群采用基本FOA算法进行全局搜索,较好地平衡局部搜索能力和全局搜索能力;两个子群间的信息通过全局最优个体的更新和种群个体的重组进行交换.DDSCFOA算法能跳出局部极值,避免陷入局部最优.仿真结果表明,动态双子群协同进化的策略有效可行,DDSCFOA算法比基本FOA算法具有更好的优化性能.

In order to overcome the demerits of basic Fruit Fly Optimization Algorithm （ FOA）, such as low convergence precision and easily relapsing into local optimum, a dynamic double subgroup cooperative Fruit Fly Optimization Algorithm （DDSCFOA） is presented. Firstly, the whole group is dynamically divided into advanced subgroup and backward subgroup according to its own evolutionary level. Secondly, a finely local searching is made for advanced subgroup in the neighborhood of local optimum with Chaos algorithm, and a global search with FOA is made for backward subgroup, so that tha whole group keeps in good balance between the global searching ability and local searching ability. Finally, two subgroups exchange information by updating the overall optimum and recombining the subgroups. DDSCFOA can jump out of local optimum and avoid falling into local optimum. The experimental results show that the strategy of dynamic double subgroup cooperative evolution is effective and feasible, DDSCFOA is much better than basic FOA in convergence velocity and convergence precision.