Optimized fuzzy clustering by predator prey particle swarm optimization
Wooseok Jang, Hwan-Il Kang, Byung-hee Lee, Kab Il Kim, Dong-il Shin, Seung-chul Kim · 2007
In this paper, we focus on the optimization of fuzzy clustering. Particle Swarm Optimizations (PSO) is used for optimizing the algorithms. PSO is an algorithm which takes a cue from nature’s bird flock or fish school and is known to have superior ability in search and fast convergence. But it might be difficult to find global optimal solution of the fuzzy clustering when it comes to complex higher dimensions. So we optimize the fuzzy clustering using Predator Prey Particle Swarm Optimizations (PPPSO). The concept of PPPSO is that predators chase the center of prey’s swarm, and preys escape from predators, in order to avoid local optimal solutions and find global optimal solution efficiently. The performance of fuzzy c-means (FCM), particle swarm fuzzy clustering (PSFC) and predator prey particle swarm fuzzy clustering (PPPSFC) are compared. Through experiments, we show that the proposed algorithm has the best performance among them.