File Exchange

image thumbnail

Weighted Differential Evolution Algorithm (WDE)

version 1.0.4 (446 KB) by GeoMath
A new evolutionary search algorithm, i.e., Weighted Differential Evolution Algorithm (WDE), has been presented.


Updated 10 Sep 2019

View License

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.

Cite As

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).

Comments and Ratings (11)

Check out my lastest framework for benchmark functions using python (numpy).

Dear, I was wondering where I could find the 28 CEC'2013 testing functions you used to examine your WDE. I just download your matlab code but could not find the benchmark functions for F1-F28




Dear users, you can also try a other extermly robust evolutionary search method : BSA. Please see for clear-code of BSA :


Dear MATLABFACE, please use Matlab2018b. Rou will see that the code works correctly. Regards.


the code can not run!
错误使用 -

出错 my_3Dgps_network (line 33)
v=v-mean(v); % save centeroid of geometry

出错 algo_wde (line 53)
fitP = feval(fnc,P,mydata);


probably you have already seen my contribution I contains an implementation of the Differential Evolution algorithm but 99% of the code is parallelization, reporting and visualization of the optimization problem. It also contains handling of quantization, integer parameters and parameter boundaries.

My contribution also contains an interface for other optimization algorithms in the file computenewpopulation.m. It would be cool if you would add an adaptation of that file to your package (or to mine, doesn't matter) in order to connect the benefits of both contributions.


akif gunen

David Lee



benchmark problems have been updated




cite information is supplied.


Latex file of WDE has been supplied.

MATLAB Release Compatibility
Created with R2018a
Compatible with R2018b
Platform Compatibility
Windows macOS Linux