NDLI: Differential Evolution strategy based on the constraint of fitness values classification

Content Provider	IEEE Xplore Digital Library
Author	Zhihui Li Zhigang Shang Qu, B.Y. Liang, J.J.
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou, China (Zhihui Li; Zhigang Shang; Liang, J.J.) \|\| Sch. of Electr. & Inf. Eng., Zhongyuan Univ. of Technol., Zhengzhou, China (Qu, B.Y.)
Abstract	This paper presents a new Differential Evolution (DE) strategy, named as FCDE, based on the constraint of classification of fitness function values. To ensure the population could move to the better fitness landscape, the global fitness value distribution information of the objective function are used and all points in the population are classified into three class by their fitness values in each generation, so the points in each class choose their donor vector and differential vector from the points in adjacent senior class to form the trial vector. This strategy could speed up the convergence to global optimal as well as avoid falling into the local optimal. Another attractive character of FCDE is the control parameters in this DE variant are self-adaptive. This method is tested on the 30 benchmark functions of CEC2014 special session and competition on single objective real-parameter numerical optimization. The experimental results showed acceptable reliability of this strategy in high search dimension. This paper will participate in the competition on real parameter single objective optimization to compare with other algorithms.
Starting Page	1454
Ending Page	1460
File Size	1557744
Page Count	7
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900507
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Sociology Statistics Support vector machine classification Linear programming Optimization Classification algorithms Classification Differential Evolution Constraint optimization Fitness Values
Content Type	Text
Resource Type	Article

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