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Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution

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Artificial Life and Computational Intelligence (ACALCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10142))

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Abstract

The size, scope and variety of the experimental analyses of metaheuristics has increased in recent years, aiming to develop new procedures and techniques to improve our understanding of optimization algorithms and problems. In this paper, we compare particle swarm optimization and differential evolution on a set of real-world clustering problems. Generally, experimental comparisons focus on presenting a statistical summary of algorithm performance, however this hides valuable information about the algorithm behaviour on the problems in question. Instead, we take an exploratory approach, focussing on extracting deeper insights and understanding from the experimental results data. We make progress on understanding the fitness landscapes of the set of clustering problems, as well as analysing current and previous experimental results for algorithms applied to these problems. Consequently, the paper makes two contributions: (a) Advancing our understanding of what factors make this set of problem instances easy or hard for given algorithms; (b) Demonstrating the need to be careful in experimental evaluations and that better insights can be obtained with exploratory analysis.

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, University of Queensland, Q. 4702, Queensland, Australia

    Sobia Saleem & Marcus Gallagher

Authors
  1. Sobia Saleem
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  2. Marcus Gallagher
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Correspondence to Sobia Saleem .

Editor information

Editors and Affiliations

  1. The University of Adelaide, Adelaide, South Australia, Australia

    Markus Wagner

  2. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  3. Swinburne University, Melbourne, Victoria, Australia

    Tim Hendtlass

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Saleem, S., Gallagher, M. (2017). Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-51691-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51690-5

  • Online ISBN: 978-3-319-51691-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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