Parallel social spider clustering algorithm for high dimensional datasets

摘要

The Social-Spider Optimization (SSO) is one of the recently developed swarm intelligence. It is inspired from the social behavior of spiders living in huge colonies. In this manuscript, a parallel version of this algorithm is formulated and termed as P-SSO by making a position update process of spiders (female, dominant and non-dominant male) running simultaneously. Simulation studies on cluster analysis of benchmark high-dimensional datasets using proposed P-SSO are found to be nearly 10 times computationally faster than the original version of SSO. Comparative analysis with other standard parallel version algorithms like Adaptive Parallel Particle Swarm Optimization (PPSO), Real Coded Parallel Genetic Algorithm (RCPGA) and K-means reveals the superior clustering accuracy of the proposed method. The designed algorithm is also tested on real life application where multi-spectral image segmentation is formulated as a clustering problem. The images are taken from NASA landsat 8. It covers an area of 200 km of Northwest Chennai obtained before and after flood conditions. This is done to analyze the flood severity. The overall accuracy of P-SSO is highest among all the methods. Also, in case of detection of water flooded areas the producer's accuracy is 76.89% which is two times better than K-means.