Improving differential evolution with impulsive control framework

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in many areas. In this paper, an impulsive control method is introduced to the DE framework, and the impulsive DE (IpDE) is proposed for improving the performance of DE. The impulsive control operation instantly moves the individuals which do not update for continuous pre-defined generations to a desired state based on the individuals with better fitness values in the current population. This way, IpDE controls individuals' positions in the space domain according to the stagnation status of the population. In order to validate the effectiveness of IpDE, the presented framework is applied to the original DE algorithms, as well as several state-of-the-art DE variants. Experimental results exhibit that IpDE is a simple but effective framework to improve the performance of the studied DE algorithms.