Addressing Premature Convergence with Distance based Parameter Adaptation in SHADE
Adam Viktorin, Roman Šenkeřík, Michal Pluháček, Tomáš Kadavý · 2018
In this paper, an analysis of a distance based parameter adaptation in Success-History based Differential Evolution (SHADE) is presented in order to show that it can have a beneficial effect on the premature convergence of the algorithm. The premature convergence of SHADE is an issue mainly in higher dimensional decision spaces. Therefore, the tests are done on the basis of CEC2015 benchmark in 10D, 30D and 50D. The results are provided and discussed.