A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions' fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models.

Automated summary

In this paper, a novel surrogate-assisted evolutionary algorithm is proposed, which employs a surrogate model to conduct pairwise comparisons between candidate solutions instead of directly predicting solutions' fitness values. Compared to regression and classification models, the proposed model based on pairwise comparison can better balance between positive and negative samples, and be directly used, reversely used, or ignored based on its reliability in model management. The experimental results on abundant benchmark and real-world problems demonstrate that the proposed surrogate model is more accurate and outperforms state-of-the-art surrogate models.