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Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
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
2016 (English)In: Journal of Artificial Intelligence and Soft Computing Research, ISSN 2449-6499, Vol. 6, no 2, 103-118 p.Article in journal (Refereed) Published
Abstract [en]

Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

Place, publisher, year, edition, pages
2016. Vol. 6, no 2, 103-118 p.
Keyword [en]
Differential evolution, Optimization, Parameter adaptation
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-34771DOI: 10.1515/jaiscr-2016-0009ScopusID: 2-s2.0-85009725987OAI: oai:DiVA.org:mdh-34771DiVA: diva2:1072682
Available from: 2017-02-08 Created: 2017-02-02 Last updated: 2017-02-08Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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