Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules

Michelle Galea, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter


An approach based on Ant Colony Optimization for the induction of fuzzy rules is presented. Several Ant Colony Optimization algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific class. The final outcome is a fuzzy rulebase that has been evolved so that individual rules complement each other during the classification process. This novel approach to fuzzy rule induction is compared against several other fuzzy rule induction algorithms, including a fuzzy genetic algorithm and a fuzzy decision tree. The initial findings indicate comparable or better classification accuracy, and superior comprehensibility. This is attributed to both the strategy of evolving fuzzy rules simultaneously, and to the individual rule discovery mechanism, the Ant Colony Optimization heuristic. The strengths and potential of the approach, and its current limitations, are discussed in detail.
Original languageEnglish
Title of host publicationSwarm Intelligence in Data Mining
EditorsAjith Abraham, Crina Grosan, Vitorino Ramos
PublisherSpringer Berlin Heidelberg
Number of pages25
ISBN (Electronic)978-3-540-34956-3
ISBN (Print)978-3-540-34955-6
Publication statusPublished - 2006

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Berlin Heidelberg
ISSN (Print)1860-949X


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