Opposition-based electromagnetism-like for global optimization

Erik Cuevas, Diego Oliva, Daniel Zaldivar, Marco Perez-Cisneros, Gonzalo Pajares

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Electromagnetism-like Optimization (EMO) is a global optimization algorithm, particularly well-suited to solve problems featuring non-linear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact with each other according to electromagnetism's laws of attraction and repulsion. However, EMO usually requires a large number of iterations for a local search procedure; any reduction or cancelling over such number, critically perturb other issues such as convergence, exploration, population diversity and accuracy. This paper presents an enhanced EMO algorithm called OBEMO, which employs the Opposition-Based Learning (OBL) approach to accelerate the global convergence speed. OBL is a machine intelligence strategy which considers the current candidate solution and its opposite value at the same time, achieving a faster exploration of the search space. The proposed OBEMO method significantly reduces the required computational effort yet avoiding any detriment to the good search capabilities of the original EMO algorithm. Experiments are conducted over a comprehensive set of benchmark functions, showing that OBEMO obtains promising performance for most of the discussed test problems.

Original languageEnglish
Pages (from-to)8181-8198
Number of pages18
JournalInternational Journal of Innovative Computing, Information and Control
Volume8
Issue number12
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Electromagnetism-like optimization
  • Global optimization
  • Opposition-based learning

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Opposition-based electromagnetism-like for global optimization'. Together they form a unique fingerprint.

Cite this