A framework for inverse surrogate modeling for fitness estimation applied to Multi-Objective Evolutionary Algorithms

Many-Objective Optimization Problems, or MaOPs, are complex optimization problems with more than three objective functions. Traditional Multi-Objective Evolutionary Algorithms (MOEAs) have shown poor scalability in solving this problem. Neuroevolution techniques can be used to overdraw that problem. The use of machine learning techniques to enhance optimization algorithms applied to MaOPs has drawn attention due to their capacity to add domain knowledge during the search process. One method of this kind is inverse surrogate modeling for fitness estimation, which uses machine learning models to enhance MOEAs, mapping the objective function values to the decision variables. Some inverse modeling methods in literature have performed well in various optimization problems. Despite the promising results, new strategies using the generated knowledge during the search can be further improved. The main goal of this work is to propose a framework that uses an inverse modeling approach coupled with an MOEA. The inverse surrogate modeling is used to sample solutions that are combined with the evolutionary search of the MOEA. The framework is evaluated in different scenarios, including multi-objective benchmark problems and real-world problems. The results show that the proposed framework had statistically better or equivalent results in 86% scenarios in the benchmark problems and 100% scenarios in the real-world problem. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This study aims to propose a framework that combines an inverse modeling approach with multi-objective evolutionary algorithms to enhance the capability of solving many-objective optimization problems. The results show that the proposed framework outperforms or performs equally well as traditional methods in various scenarios, including benchmark problems and real-world problems.