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Using Random Local Search Helps in Avoiding Local Optimum in Differential Evolution
Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0001-9857-4317
2014 (English)In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2014, Innsbruck, Austria, 2014, 413-420 p.Conference paper, Published paper (Refereed)
Abstract [en]

Differential Evolution is a stochastic and metaheuristic technique that has been proved powerful for solving real valued optimization problems in high dimensional spaces. However, Differential Evolution does not guarantee to con verge to the global optimum and it is easily to become trapped in a local optimum. In this paper, we aim to enhance Differential Evolution with Random Local Search to increase its ability to avoid local optimum. The proposed new algorithm is called Differential Evolution with Random Local Search (DERLS). The advantage of Random Local Search used in DERLS is that it is simple and fast in computation. The results of experiments have demonstrated that our DERLS algorithm can bring appreciable improvement for the acquired solutions in difficult optimization problems.

Place, publisher, year, edition, pages
Innsbruck, Austria, 2014. 413-420 p.
Keyword [en]
Differential Evolution, random local search, local search, evolutionary algorithms, local optimum, global optimization
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-25153DOI: 10.2316/P.2014.816-021Scopus ID: 2-s2.0-84898444101OAI: oai:DiVA.org:mdh-25153DiVA: diva2:722695
Conference
IASTED 13th International Conference on Artificial Intrelligence and Applications AIA 2014, 17-19 Feb 2014, Innsbruck, Austria
Projects
EMOPAC - Evolutionary Multi-Objective Optimization and Its Applications in Analog Circuit Design
Available from: 2014-06-09 Created: 2014-06-05 Last updated: 2016-10-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
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Output format
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