Parallel Social Spider Optimization Algorithms with Island Model for the Clustering Problem

Abstract

The digital age came with an extraordinary ability to generate data across organizations, people, and devices, data that needs to be analyzed, processed and stored. A well-known technique for analyzing this kind of data is Clustering. Many bio-inspired algorithms were proposed for this problem such as the Social Spider Optimization (SSO). In this work, we propose parallel island models of the SSO algorithm for the Clustering problem, using 24 processors for each parallel algorithm. Such models were implemented using static and dynamic topologies, and datasets from the UCI Machine Learning Repository used for the stage of experiments. The achieved average speedups range from 15 to 28 times faster than the SSO algorithm for large and small datasets, respectively, and a parallel model with static ring topology performs a little bit faster than the other parallel models. The parallel algorithms provide results with similar precision to the ones computed with the SSO algorithm.