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Bernstain-Search Differential Evolution Algorithm

version 1.0.3 (3.16 MB) by GeoMath
A new high-performance differential evolution algorithm has been presented.


Updated 07 Aug 2019

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Parameter setting in standard Differential Evolution Algorithm (DE) is a time-consuming phase since it is based on a trial-and-error process. In this paper, a parameter-free DE algorithm, i.e. Bernstain-Search Differential Evolution Algorithm (BSD), has been proposed for real valued numerical optimization problems. Since BSD's parameter values are determined randomly, it is practically parameter-free. Therefore, the BSD does not have a parameter setting process, contrary to DE and its improved versions. In this paper, 30 benchmark problems of CEC'2014, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) evolution problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four comparison algorithms (i.e., ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and comparison algorithms were statistically compared by using Wilcoxon Signed Rank Test piece wisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the comparison algorithms that have been used in this paper.

Cite As

Civicioğlu P., Besdok E., Bernstain-Search Differential Evolution Algorithm for Numerical Function Optimization, Expert Systems with Applications, Available online 24 July 2019, 112831, (In Press, Accepted Manuscript). please see for the manuscript :

MATLAB Release Compatibility
Created with R2018b
Compatible with R2018b
Platform Compatibility
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