Clustering research of fabric deformation comfort using bi-swarm PSO algorithm

This paper proposes a new approach to using particle swarm optimization (PSO) algorithm to cluster fabric comfort. It is shown how PSO can be used to cluster the deformation comfort data by according to the similarity of the deformation comfort characteristics and the desired cluster number. The swarm was divided into two subgroups, and the inertia weight of each subgroup dynamically changed along with the iterative generations and fitness value respectively. The new algorithm was evaluated on data sample, and the clustering center was seen as the solution of the particle. The analysis of the clustering results and the comparison of fuzzy cluster results and PSO-based cluster results show that the proposed algorithm has great practical value and ability to overcome the disadvantages of fuzzy cluster which depends on the human experience and cannot cluster according to the desired cluster number directly.