NDLI: Improved Differential Evolutions Using a Dynamic Differential Factor and Population Diversity

Content Provider	IEEE Xplore Digital Library
Author	Jixiang Cheng Gexiang Zhang
Copyright Year	2009
Abstract	As a new kind of evolutionary algorithms, differential evolution (DE) has attracted much attention in solving optimization problems in the last few years. To accelerate its convergence rate and enhance its performances, this paper introduces a dynamic adjustment method for the differential factor and a modified version of mutation strategy into DE. Furthermore, a disturbance approach based on population diversity is used to further improve the search capability. Thus, two improved DE, IDE1 and IDE2, are presented. The performances of the IDE1 and IDE2 are evaluated on seven complex benchmark functions with three different dimensionalities. Experimental results show that the performances of IDE1 and IDE2 are superior to other two DEs in terms of convergence rates and qualities of solutions.
Starting Page	402
Ending Page	406
File Size	346466
Page Count	5
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Differential strategy Genetic mutations Optimization methods Evolutionary computation Information science Evolution (biology) Differential evolution Performance analysis Acceleration Differential factor Artificial intelligence Computational intelligence Population diversity
Content Type	Text
Resource Type	Article

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