Improving differential evolution with impulsive control framework

Wei Du, Sunney Yung Sun Leung, Chun Kit Kwong, Yang Tang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Number of pages8
ISBN (Electronic)9781479974924
Publication statusPublished - 10 Sept 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 25 May 201528 May 2015


ConferenceIEEE Congress on Evolutionary Computation, CEC 2015

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mathematics


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