A new particle swarm optimization algorithm for dynamic image clustering
Salima Ouadfel, Mohamed Batouche, Abdelmalik Taleb‐Ahmed · 2010
In this paper, we present ACPSO a new dynamic image clustering algorithm based on particle swarm optimization. ACPSO can partition image into compact and well separated clusters without any knowledge on the real number of clusters. It uses a swarm of particles with variable number of length, which evolve dynamically using mutation operators. Experimental results on real images demonstrate that the proposed algorithm is able to extract the correct number of clusters with denser and more compactness clusters. The results demonstrate that ACPSO outperforms other optimization algorithms.