An enhanced distance-based parameter adaptation multi-population ensemble differential evolution
Xiangping Li, Yingqi Huang · 2024
Usually, mutation strategies and the corresponding parameter values play an important role in DE. Because different mutation strategies reveal different characteristic during the course of the evolution, combing different mutation strategies has been one of the research direction for DE improvement. In the work, a novel algorithm (eD-MPEDE) is introduced, which applies distance based parameter adaptation to produce competitive trial vectors. Furthermore, the candidate pool of eD-MPEDE contains three novel mutation strategies with different characteristics, which help to balance exploratory and convergence. Tests on CEC2017 show that eD-MPEDE is efficient to obtain promising solutions.