DeMat

DeMat: Differential Evolution (DE) in MATLAB. The latest MATLAB ® code from the book Differential Evolution - A Practical Approach to Global Optimization is available here by courtesy of Springer publisher. The code is designed to incorporate bounds, inequality, and equality constraints. The above book contains a detailed explanation of the code and some more examples in the CD companion. However, the code for download here contains the main engine in its full functionality for experimentation. Special attention has been given to coding conventions in hungarian prefix notation to make the programs easier to understand and use.


References in zbMATH (referenced in 41 articles , 1 standard article )

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  1. Kristoffersen, Brage S.; Bellout, Mathias C.; Silva, Thiago L.; Berg, Carl F.: An automatic well planner for complex well trajectories (2021)
  2. Massulini Acosta, Simone; Amoroso, Anderson Levati; Oliveira Sant Anna, Angelo Marcio; Canciglieri Junior, Osiris: Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process (2021)
  3. Chacón Castillo, Joel; Segura, Carlos: Differential evolution with enhanced diversity maintenance (2020)
  4. Liu, Pengfei; Cao, Hongsong; Feng, Shunshan; Liu, Hengzhu; Du, Ye: Study on the application scheme of aerodynamic coefficient identification based on the differential evolution algorithm (2020)
  5. Natkunam, Kokulan; Hai, Yang; George, Erwin; Lai, Choi-Hong; Liu, Li: Simultaneous estimation of tracer kinetic model parameters using analytical and inverse approaches with a hybrid method (2019)
  6. Suzuki, Taiji: Fast learning rate of non-sparse multiple kernel learning and optimal regularization strategies (2018)
  7. Mekh, M. A.; Hodashinsky, I. A.: Comparative analysis of differential evolution methods to optimize parameters of fuzzy classifiers (2017)
  8. Rivera-Lopez, Rafael; Canul-Reich, Juana: A global search approach for inducing oblique decision trees using differential evolution (2017)
  9. Villarreal-Cervantes, Miguel G.: Approximate and widespread Pareto solutions in the structure-control design of mechatronic systems (2017)
  10. Atanassov, Krassimir: Generalized nets as a tool for the modelling of data mining processes (2016)
  11. Baeyens, Enrique; Herreros, Alberto; Perán, José R.: A direct search algorithm for global optimization (2016)
  12. Hao, Jing-hua; Liu, Min; Lin, Jian-hua; Wu, Cheng: A hybrid differential evolution approach based on surrogate modelling for scheduling bottleneck stages (2016)
  13. Locatelli, Marco; Vasile, Massimiliano: (Non) convergence results for the differential evolution method (2015)
  14. Silva, Tiago; Loja, Maria; Maia, Nuno; Barbosa, Joaquim: A hybrid procedure to identify the optimal stiffness coefficients of elastically restrained beams (2015)
  15. Kotinis, Miltiadis: Improving a multi-objective differential evolution optimizer using fuzzy adaptation and (K)-medoids clustering (2014) ioport
  16. Olofintoye, Oluwatosin; Adeyemo, Josiah; Otieno, Fred: A combined Pareto differential evolution approach for multi-objective optimization (2014) ioport
  17. Boussaïd, Ilhem; Lepagnot, Julien; Siarry, Patrick: A survey on optimization metaheuristics (2013)
  18. Safari, Amir; Lemu, Hirpa G.; Jafari, Soheil; Assadi, Mohsen: A comparative analysis of nature-inspired optimization approaches to 2D geometric modelling for turbomachinery applications (2013) ioport
  19. Zhang, Rui; Song, Shiji; Wu, Cheng: A simulation-based differential evolution algorithm for stochastic parallel machine scheduling with operational considerations (2013)
  20. Ahandani, Morteza Alinia; Alavi-Rad, Hosein: Opposition-based learning in the shuffled differential evolution algorithm (2012) ioport

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