An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness implementation difficulty of systems, an attractive membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors crossover operators distributed parallel computing model monodirectional tissue-like systems (MTP), is constructed proposed, which simply named ECPSO-MTP. proposed ECPSO-MTP, two kinds evolution rules objects are defined introduced rewrite modify velocity in different elementary membranes. The updating uses partitioning information randomly replaces global best improve performance operator position given other probability accomplished through hybridization membranes reject randomness. structure ECPSO-MTP abstracted as network structure, exchange resource sharing between by evolutional symport promoters MTP, including forward backward communication rules. mechanisms executed repeatedly iteration. At last, comparison experiments, conducted eight benchmark datasets from artificial UCI Machine Learning Repository image segmentation BSDS500, demonstrate effectiveness