A weighted minimum distance using hybridization of particle swarm optimization and Bacterial Foraging

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2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationPRICAI 2014: Trends in Artificial Intelligence
EditorsDuc-Nghia Pham, Seong-Bae Park
Number of pages11
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event13th Pacific Rim International Conference on Artificial Intelligence - Gold Coast, Australia
Duration: 1 Dec 20145 Dec 2014
Conference number: 13th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
ISSN (Print)0302-9743


Conference13th Pacific Rim International Conference on Artificial Intelligence
Abbreviated title13th PRICAI (2014)
CityGold Coast
Internet address


  • Bacterial Foraging
  • Particle Swarm Optimization
  • Symbolic aggregate approximation
  • Time series data mining
  • Weighted Minimum Distance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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