用于连续域寻优的分组蚁群算法

用蚁群算法进行多模函数优化时，容易陷入局部最优，从而影响了寻优精度和收敛速度。因此提出了一种用于求解连续空间优化问题的分组蚁群算法。该算法将连续空间优化问题的定义域划分成若干个子区域，并给每个子区域分配一组蚂蚁。每组蚂蚁在各自的区域里进行搜索，且在搜索过程采用“精英策略”并利用精英蚂蚁更新普通蚂蚁的位置信息，以加快算法的收敛速度。同时，当普通蚂蚁离精英蚂蚁之间的距离较长时，使用大步长搜索，以加快搜索速度，反之，采用小步长搜索，可提高搜索过程的精细程度。该方法使每组蚂蚁的搜索空间成倍地缩小并能有效地改善陷入局部最优的情况，从而能使收敛速度和精度大幅提高。计算机的仿真实验结果证实了这一结论。

Grouped ant colony algorithm for relying continuous optimization problems

Ant colony algorithm is easy to fall into local optimum when it solves multi-optimum function optimization problem,which impacts the accuracy and convergence speed.Therefore this paper presents grouped ant colony algorithm for solving continuous optimization problems.The algorithm divides the definition domain into several sub-regions,and gives each sub-region a set of ants.Ants of each region search in their domain,and in the search process the algorithm uses the ＂elite strategy＂ to update the location information of ordinary ants,the strategy can speed up convergence speed.At the same time,when the elite is far from the ordinary ants the algorithm uses big move search to accelerate the search speed,on the contrary,uses small move search to improve the level of the fine.The method narrows the search space multiply and it can effectively improve the situation of a local optimum,thus convergence speed and accuracy can be significantly improved.The results of computer simulation confirm this conclusion.