We propose two surrogate-based strategies for increasing the convergence speed of multi-objective evolutionary algorithms (MOEAs) by stimulating the creation of high-quality individuals early in the run. Both offspring generation strategies are designed to leverage the fitness approximation capabilities of light-weight interpolation-based models constructed using an inverse distance weighting function. Our results indicate that for the two solvers we tested with, NSGA-II and DECMO2++, the application of the proposed strategies delivers a substantial improvement of early convergence speed across a test set consisting of 31 well-known benchmark problems.
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2022. Lightweight Interpolation-based surrogate modelling for multiobjective continuous optimisation. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 53-60. Available from: https://doi.org/10.1007/978-3-031-25312-6_6