基于混合蛙跳算法的聚类问题研究

混合蛙跳算法(SFLA)是一种基于子群——种群进化模式的群智能优化算法,通过自身特有的分组算子实现不同解的合理分布,能够有效跳出局部最优;局部位置更新算子只对最差解进行更新,加快了算法的收敛速度。针对传统算法在解决聚类问题时存在聚类精度低和算法收敛速度较慢等缺点,本文提出了运用混合蛙跳算法来解决聚类问题,通过采用基于图像二维空间像素特征提取的方法构造青蛙个体解,设计青蛙进化的目标函数和青蛙位置更新策略,并通过数字,图形等验证了该算法解决聚类问题的有效性.

Research of clustering problem based on shuffled frog leaping algorithm

The shuffled frog Leaping algorithm is a kind of intelligent optimization algorithms based on subgroup-group evolution model,using their own unique group operator to realize reasonable distribution of different solutions,which can jump out of local optimal effectively.The algorithm of Local position only updates the worst solution,accelerating the algorithm convergence speed.Traditional algorithm existing lower clustering cluster precision and slower algorithm convergence speed shortcomings,this paper puts forward shuffled frog leaping algorithm to solve the clustering problem,by the use of the 2 d space based on image pixels feature extraction to structure frog individual,design the frog evolution target function and the frog location updating strategy,and through the numbers,graphics to prove the validity of the clustering problem.