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Investigation of mutation strategies in differential evolution for solving global optimization problems
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
2014 (English)In: Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I, Springer, 2014, no PART 1, p. 372-383Chapter in book (Refereed)
Abstract [en]

Differential evolution (DE) is one competitive form of evolutionary algorithms. It heavily relies on mutating solutions using scaled differences of randomly selected individuals from the population to create new solutions. The choice of a proper mutation strategy is important for the success of an DE algorithm. This paper presents an empirical investigation to examine and compare the different mutation strategies for global optimization problems. Both solution quality and computational expense of DE variants were evaluated with experiments conducted on a set of benchmark problems. The results of such comparative study would offer valuable insight and information to develop optimal or adaptive mutation strategies for future DE researches and applications. 

Place, publisher, year, edition, pages
Springer, 2014. no PART 1, p. 372-383
Keywords [en]
Differential Evolution, Evolutionary Algorithm, Global Optimization Problem, Mutation Strategies
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-25594DOI: 10.1007/978-3-319-07173-2_32Scopus ID: 2-s2.0-84902590522ISBN: 9783319071725 (print)OAI: oai:DiVA.org:mdh-25594DiVA, id: diva2:732637
Conference
1 June 2014 through 5 June 2014, Zakopane
Available from: 2014-07-04 Created: 2014-07-04 Last updated: 2016-10-26Bibliographically approved

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Xiong, Ning

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