A particle filtering-based estimation of distribution algorithm for multi-objective optimisation

Xiaoran Shi, Nurcin Celik

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A novel particle filtering-based estimation of distribution algorithm (EDA) is proposed to address multi-objective optimisation problems. Specifically, the particles drawn from a sampling distribution are considered as the candidate solutions. This sampling distribution is computed recursively based on the performance of the prior particle set and the newly arrived observations. As the iteration progresses, the distribution function gradually concentrates on the promising region(s) of the solution space, indicating higher probabilities to obtain solutions with good performances in terms of the objective values. In order to validate the performance of the proposed algorithm, a case study of an environmental economic load dispatch (EELD) is conducted where the bi-objective EELD optimisation problem is solved via the proposed algorithm, and the performance of the proposed algorithm is benchmarked against several algorithms studied in the literature. Experimental results have revealed that the proposed algorithm produces very promising results against those in the literature.

Original languageEnglish (US)
Pages (from-to)176-191
Number of pages16
JournalInternational Journal of Simulation and Process Modelling
Volume11
Issue number3-4
DOIs
StatePublished - Jan 1 2016

Keywords

  • EDA
  • Estimation of distribution algorithm
  • Multi-objective optimisation
  • Particle filtering

ASJC Scopus subject areas

  • Computer Science Applications
  • Applied Mathematics
  • Modeling and Simulation

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