mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE
Mälardalen University, School of Innovation, Design and Engineering, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2018 (English)In: 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

JADE is an state-of-the-art adaptive differential evolution algorithm which implements 'DE/current-to-pbest' as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
adaptive parameter, coarse-grained parallel, Differential evolution, evolutionary optimization, global optimization, multi-population, population topology, Calculations, Silicate minerals, Adaptive parameters, Coarse-grained parallels, Evolutionary optimizations, Multi population, Population topologies, Evolutionary algorithms
National Category
Embedded Systems Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-41391DOI: 10.1109/CEC.2018.8477764Scopus ID: 2-s2.0-85056280928ISBN: 9781509060177 (print)OAI: oai:DiVA.org:mdh-41391DiVA, id: diva2:1362206
Conference
2018 IEEE Congress on Evolutionary Computation, CEC 2018, 8 July 2018 through 13 July 2018
Available from: 2019-10-18 Created: 2019-10-18 Last updated: 2019-10-18

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Leon, MiguelXiong, Ning

Search in DiVA

By author/editor
Leon, MiguelXiong, Ning
By organisation
Embedded Systems
Embedded SystemsComputer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 4 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf