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Search Trajectory Networks of Population-Based Algorithms in Continuous Spaces

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

We introduce search trajectory networks (STNs) as a tool to analyse and visualise the behaviour of population-based algorithms in continuous spaces. Inspired by local optima networks (LONs) that model the global structure of search spaces, STNs model the search trajectories of algorithms. Unlike LONs, the nodes of the network are not restricted to local optima but instead represent a given state of the search process. Edges represent search progression between consecutive states. This extends the power and applicability of network-based models to understand heuristic search algorithms. We extract and analyse STNs for two well-known population-based algorithms: particle swarm optimisation and differential evolution when applied to benchmark continuous optimisation problems. We also offer a comparative visual analysis of the search dynamics in terms of merged search trajectory networks.

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Author information

Authors and Affiliations

  1. University of Stirling, Scotland, UK

    Gabriela Ochoa

  2. Department of Decision Sciences, University of South Africa, Pretoria, South Africa

    Katherine M. Malan

  3. Artificial Intelligence Research Institute (IIIA-CSIC), Campus of the UAB, Bellaterra, Spain

    Christian Blum

Authors
  1. Gabriela Ochoa
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  2. Katherine M. Malan
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  3. Christian Blum
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Corresponding author

Correspondence to Gabriela Ochoa .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Ochoa, G., Malan, K.M., Blum, C. (2020). Search Trajectory Networks of Population-Based Algorithms in Continuous Spaces. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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