MULTI-POPULATION BASED DIFFERENTIAL EVOLUTION ALGORITHM
The Multi-population Based Differential Evolution Algorithm (MDE) has been proposed to solve real-valued numerical optimization problems.
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In this paper, the Multi-population Based Differential Evolution Algorithm (MDE) has been proposed to solve real-valued numerical optimization problems with its convergence proof. The mutation operator of MDE is partial—elitist and its crossover operator is parameter-free, in practice. In this paper, 28 benchmark problems of CEC2013 with Dim = 20 and one real-world geometric optimization problem have been used in the experiments performed to examine the numerical problem-solving success of MDE. MDE's success in solving related benchmark problems has been statistically compared with ABC, CK, SOS and GWO. Statistical analysis of the results obtained from the experiments exposed that MDE is statistically more successful than comparison methods in solving numerical optimization problems used.
Cite As
AEKARKINLI (2026). MULTI-POPULATION BASED DIFFERENTIAL EVOLUTION ALGORITHM (https://se.mathworks.com/matlabcentral/fileexchange/119988-multi-population-based-differential-evolution-algorithm), MATLAB Central File Exchange. Retrieved .
Karkinli, Ahmet Emin. “Detection of Object Boundary from Point Cloud by Using Multi-Population Based Differential Evolution Algorithm.” Neural Computing and Applications, Springer Science and Business Media LLC, Oct. 2022, doi:10.1007/s00521-022-07969-w.
General Information
- Version 1.0.04 (3.47 KB)
MATLAB Release Compatibility
- Compatible with R2020a to R2022b
Platform Compatibility
- Windows
- macOS
- Linux
