Weighted Differential Evolution Algorithm (WDE)

Version 1.0.4 (446 ko) par GeoMath
A new evolutionary search algorithm, i.e., Weighted Differential Evolution Algorithm (WDE), has been presented.
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Mise à jour 10 sept. 2019

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In this paper, Weighted Differential Evolution Algorithm (WDE) has been proposed for solving real valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its nonrecursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE’s success in solving CEC'2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS Network Adjustment Problem) and 4 constrained engineering design problems were used to examine the WDE’s ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

Citation pour cette source

P Civicioglu, E Besdok, MA Gunen, UH Atasever, (2018), Weighted Differential Evolution Algorithm for Numerical Function Optimization ; A Comparative Study with Cuckoo Search, Artificial Bee Colony, Adaptive Differential Evolution, and Backtracking Search Optimization Algorithms, Neural Comput & Applic (2018). https://doi.org/10.1007/s00521-018-3822-5

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Créé avec R2018a
Compatible avec R2018b
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wde/cc_wde/3D-baseline adjustment

wde/cc_wde/PointCloudProcessing

wde/cc_wde/affine_transform

wde/cc_wde/classic_benchmark_problems

wde/cc_wde/statistical distribution fitting by WDE

Version Publié le Notes de version
1.0.4

benchmark problems have been updated
1.0.3

.
1.0.2

cite information is supplied.
1.0.1

Latex file of WDE has been supplied.
1.0.0