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Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
On the one hand, by reducing the sensitivity of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction.
Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering
As a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization.
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy.
