A Weighted Minimum Distance Using Hybridization of Particle Swarm Optimization and Bacterial Foraging

In a previous work we used a popular bio-inspired algorithm; particle swam optimization (PSO) to improve the performance of a well-known representation method of time series data which is the symbolic aggregate approximation (SAX), where PSO was used to propose a new weighted minimum distance WMD for SAX to recover some of the information loss resulting from the original minimum distance MINDIST on which SAX is based. WMD sets different weights to different segments of the time series according to their information content, where these weights are determined using PSO. We showed how SAX in conjunction with WMD can give better results in times series classification than the original SAX which uses MINDIST. In this paper we revisit this problem and propose optimizing WMD by using a hybrid of PSO and another bio-inspired optimization method which is Bacterial Foraging (BF); an effective bio-inspired optimization algorithm in solving difficult optimization problems. We show experimentally how by using this hybrid to set the weights of WMD we can obtain better classification results than those obtained when using PSO to set these weights.