An Efficient Binary Differential Evolution with Parameter Adaptation

An Efficient Binary Differential Evolution with Parameter Adaptation

Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best indi...

Differential Evolution with Population and Strategy Parameter Adaptation

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, w...

Parameter adaptation for differential evolution with design of experiments

Optimal settings for control parameters of the Differential Evolution algorithm depend on the considered optimization problem and may also change during an optimization run. In this work an approach is suggested that adaptively controls the parameters F and CR that influence the mutation and recombination processes in Differential Evolution. By application of Design of Experiments methods signi...

Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation

Differential Evolution (DE) is a simple, yet highly competitive real parameter optimizer in the family of evolutionary algorithms. A significant contribution of its robust performance is attributed to its control parameters, and mutation strategy employed, proper settings of which, generally lead to good solutions. Finding the best parameters for a given problem through the trial and error meth...

Harmony Search Based Parameter Ensemble Adaptation for Differential Evolution