Multi-point infill sampling strategies exploiting multiple surrogate models
Paul Beaucaire, Ch. Beauthier, Caroline Sainvitu · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
This work presents interesting multi-point search algorithms exploiting several surrogate models, implemented in MI-NAMO, the multi-disciplinary optimization platform of Cenaero. Many types of surrogate models are used in the literature with their own strengths and weaknesses. More generally, each one models differently a given problem and provides its own representation of the reality. The idea of this paper is to exploit simultaneously different types of surrogate models in order to catch automatically their strengths and to outshine some of their weaknesses. This strategy is based on a multi-point enrichment at each iteration, each candidate point being provided by one kind of surrogate model and/or criterion. This strategy can be tuned by selecting different infill criteria, based on different surrogate models, in order to improve more specifically different aspects such as feasibility, exploration and/or exploitation. The performance of this surrogate-based optimization framework is illustrated on well-known constrained benchmark problems available in the literature (such as GX-functions and MOPTA08 test cases). Good performance both in terms of identification of feasible regions and objective gains is demonstrated.