关键词: 蚁群算法, 连续域寻优, 分组

Abstract: 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.

Key words: ant colony algorithm, continuous optimization, grouped