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Naïve differential evolution algorithm
WANG Shenwen, ZHANG Wensheng, QIN Jin, XIE Chengwang, GUO Zhaolu
Journal of Computer Applications    2015, 35 (5): 1333-1335.   DOI: 10.11772/j.issn.1001-9081.2015.05.1333
Abstract611)      PDF (434KB)(723)       Save

In order to solve singleness of mutation study, a naïve mutation strategy was proposed to approach the best individual and depart the worst one. So, a scale factor self-adaptation mechanism was used and the parameter was set to a small value when the dimension value of three random individuals is very close to each other, otherwise, set it to a large value. The results showed that the Differential Evolution (DE) with the new mechanism exhibits a robust convergence behavior measured by average number of fitness evaluations, successful running rate and acceleration rate.

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