基于捕食-被捕食粒子群优化的模糊聚类

粒子群优化聚类算法具有参数简单，收敛快等优势，但也有局部极值问题。为解决此问题，提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。捕食者追逐被捕食者中心，加速收敛，而被捕食者逃离捕食者，促进多样性，以防局部极值出现。实验测试数据表明，算法具有防止局部极值、收敛快、全局寻优能力强等性能优势，能够比较好客观地反映现实世界。

Fuzzy clustering based on predator prey particle swarm optimization

PSO clustering algorithm is known to have simple parameters and fast convergence,but there are also local optimal problems.To solve the problem,a fuzzy clustering based on predator prey PSO algorithm is presented,which is using density func- tion to initialize cluster centre.Predators chase preys centre,to accelerate convergence,and the prey escape predators,to promote diversity and to prevent the local optimal there.The experimental test data show that this method is limited to prevent the ex- treme,fast convergence,global optimization capabilities,and other performance advantages,better able to objectively reflect the real world.