Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning
 
conference paper

Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning

Di Mario, Ezequiel Leonardo  
•
Navarro, Inaki  
•
Martinoli, Alcherio  
2015
2015 IEEE Congress on Evolutionary Computation (CEC)
IEEE Congress on Evolutionary Computation

Particle Swarm Optimization (PSO) is a population-based metaheuristic that can be applied to optimize controllers for multiple robots using only local information. In order to cope with noise in the robotic performance evaluations, different re-evaluation strategies were proposed in the past. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of distributed PSO in the presence of noise. In particular, we compare a distributed PSO OCBA algorithm suitable for resource-constrained mobile robots with a centralized version that uses global information for the allocation. We show that the distributed PSO OCBA outperforms a previous distributed noise-resistant PSO variant, and that the performance of the distributed PSO OCBA approaches that of the centralized one as the communication radius is increased. We also explore different parametrizations of the PSO OCBA algorithm, and show that the choice of parameter values differs from previous guidelines proposed for stand-alone OCBA.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

EDM_CEC15.pdf

Type

Preprint

Version

http://purl.org/coar/version/c_71e4c1898caa6e32

Access type

openaccess

Size

326 KB

Format

Adobe PDF

Checksum (MD5)

c26b696d02b7fb292256df3bba3e2ec2

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés