Differential evolution based on covariance matrix learning and bimodal distribution parameter setting

309 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

SCA: A Sine Cosine Algorithm for solving optimization problems (2016, methodology): “ Evolutionary techniques: Genetic Algorithms (GA) [44], Differential Evolution (DE) [45-48], Biogeography-Based Optimization algorithm (BBO) [49], and Evolution Strategy (ES) [50].”
Recent advances in differential evolution - An updated survey (2016, background): “Covariance matrix based mutation and cross-over operations have been integrated in DE [203,206,67].”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “Consequently, many enhanced DE variants such as SaDE (with adapted mutation strategies and param18 eters) [39], jDE (with self-adapted parameters) [6], CoDE (composition of multiple strategies and parameter settings) [56], JADE 19 (with “current-to-pbest/1” mutation strategy and adaptive parameters)”
An efficient modified grey wolf optimizer with Lévy flight for optimization tasks (2017, methodology): “Moreover, GWO’s performance was mediocre compared to LGWO, CoBiDE, and CS. Note that the CS has the LF-based searching mechanisms as well.”
Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, methodology): “The idea of using covariance matrix learning in DE was suggested in [14].”