摘要
针对K-means易收敛于局部最优以及对初始值敏感和人工鱼群算法收敛速度快,对初始值不敏感及自组织行为的问题,提出一种K-means和人工鱼群算法融合的聚类方法。该算法先将标准人工鱼群算法用自适应策略加以改进,即在人工鱼群算法早期迭代中使用固定视野,随着迭代次数的增加,采用自适应减少的视野值。在此基础上将K-means算法融入到改进的人工鱼群算法中人工鱼中,随机产生的部分人工鱼在每次完成人工鱼群算法的迭代后,进行一次K-means算法的迭代。实验结果证明融合后的新算法明显地优于粒子群优化(PSO)、K-means及改进的人工鱼群算法(IAFSA),它将有效地被应用于数据聚类中。
Aiming at the problems of K-means clustering being subject to local optimum and sensitive to initial value, and the problems of artificial fish swarm algorithm having high convergence rate, being insensitive to initial values and self-organising behaviour, we propose a clustering method which combines K-means and artificial fish swarm algorithm. The algorithm first slightly improves the standard artificial fish swarm algorithm with self-adaptive strategy：i, e. in early iteration of artificial fish swarm algorithm it uses the fixed visual perspective, with the increase of iteration times, is adopts the self-adaptive decreasing visual perspective value. Based on this, it integrates K-means algorithm to artificial fishes of the improved artificial fish swarm algorithm, part of the artificial fishes randomly generated will go through the iteration of K-means once after finishing each iteration in artificial fish algorithm. Experimental results prove that the new algorithm is obviously superior to the particle swarm optimisation, K-means and the improved AFSA, and ig will be effectively applied in data clustering.
出处
《计算机应用与软件》
CSCD
2015年第9期240-243,279,共5页
Computer Applications and Software
基金
国家自然科学基金项目(61170120)
江苏省自然科学基金项目(BK2011147)
关键词
K—means人工鱼群算法
自组织行为
自适应策略
粒子群优化
K-means Artificial fish swarm algorithm （AFSA） Self-organising behaviour Self-adaptive strategy Particle swarm optimisation
