摘要
蚁群聚类LF算法是基于蚂蚁堆形成原理而产生的群体智能算法,存在收敛速度慢、易陷入局部最优等缺陷。为了提高LF算法的收敛速度,在算法中提供具有全局意义的记忆中心,算法运行初期,蚂蚁根据全局记忆中心的启发信息运行,随着算法的迭代,不断更新全局记忆中心。为了避免算法陷入局部最优,在全局记忆中心的指导下,每只蚂蚁向距离最小的点运动,而不是采用直接跳转的方法。新算法使用UCI数据集中的Iris和Wine验证,算法的查准率和查全率要优于其他算法。
LF ant colony clustering algorithms is swarm intelligence algorithm which is based on the principle of ant heap formation, slow to converge and easy to fall into the local optimum. In order to improve the convergence speed of the LF algorithm,memory center of global significance is provided, when the algorithm runs early, the ants run according to the heuristic information from global memory center, with the iteration of the algorithm, constantly update the global memory center. In order to avoid the algorithm into a local opti- mum, under the guidance of the global memory center, each ant moves to the minimum distance point, rather than directly jttmps. The new algorithm uses UCI dataset Iris and Wine verification, the algorithm precision rate and the recall rate is better than the other algorithms.
出处
《计算机技术与发展》
2013年第9期74-77,共4页
Computer Technology and Development
基金
陕西省高等继续教育教学改革研究项目(11J23)
关键词
蚁群聚类
全局记忆
启发信息
查准率
查全率
ant colony clustering
global memory
heuristic information
precision rate
recall rate
