An EMO improvement: Opposition-based electromagnetism-like for global optimization

Diego Oliva, Erik Cuevas

Research output: Chapter in Book/Report/Conference proceedingChapter

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 to 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 chapter 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 presented 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
Title of host publicationIntelligent Systems Reference Library
PublisherSpringer Science and Business Media Deutschland GmbH
Pages159-178
Number of pages20
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Publication series

NameIntelligent Systems Reference Library
Volume117
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

ASJC Scopus subject areas

  • Computer Science(all)
  • Information Systems and Management
  • Library and Information Sciences

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    Oliva, D., & Cuevas, E. (2017). An EMO improvement: Opposition-based electromagnetism-like for global optimization. In Intelligent Systems Reference Library (pp. 159-178). (Intelligent Systems Reference Library; Vol. 117). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-48550-8_8