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Italian Workshop on Artificial Life and Evolutionary Computation

WIVACE 2014: Advances in Artificial Life and Evolutionary Computation pp 75–85Cite as

Multi-objective Parameter Tuning for PSO-based Point Cloud Localization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 445))

Abstract

It has been largely proven that population-based metaheuristics such as Particle Swarm Optimization (PSO) are severely affected by the choice of their parameters.

In this paper, we use a multi-objective parameter tuning method called EMOPaT (Evolutionary Multi-Objective Parameter Tuning) to optimize PSO when dealing with a real-world optimization task: the localization of an object acquired by a laser scanner in the form of a point cloud.

We want to optimize both the time needed to reach a quality threshold and the final alignment between the point cloud and a reference model of the object. Our system is able to generate “fast” and “precise” versions of PSO and, among all the possible configurations which lie between the fastest and the most precise, the ones that give the best trade-offs between precision and speed.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy

    Roberto Ugolotti & Stefano Cagnoni

Authors
  1. Roberto Ugolotti
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  2. Stefano Cagnoni
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Corresponding author

Correspondence to Roberto Ugolotti .

Editor information

Editors and Affiliations

  1. CNR-ICAR, Rende, Italy

    Clara Pizzuti

  2. CNR-ICAR, Rende, Italy

    Giandomenico Spezzano

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Ugolotti, R., Cagnoni, S. (2014). Multi-objective Parameter Tuning for PSO-based Point Cloud Localization. In: Pizzuti, C., Spezzano, G. (eds) Advances in Artificial Life and Evolutionary Computation. WIVACE 2014. Communications in Computer and Information Science, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-12745-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-12745-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12744-6

  • Online ISBN: 978-3-319-12745-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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