Adaptation of Search Generations in Extreme Learning Assisted MOEA/D Based on Estimation Accuracy of Surrogate Model

In the last decade, multi-objective evolutionary algorithms (MOEAs) have been utilized for many real-world applications. However, it takes a great deal of computation time for the majority of real-world problems to obtain the optimal solutions due to the expensive fitness evaluation cost. In order to reduce the computation time for optimization, surrogate-assisted MOEAs have been studied. Our previous study analyzed ELMOEA/D, one of the surrogate-assisted MOEA combining MOEA/D with an extreme learning machine (ELM), from the relation between search performance and search generations. Our previous analysis revealed that the search generations on the surrogate space must be determined to make the accuracy of the surrogate model low. For this fact, this paper proposes the automatic adjustment methods for the search generations of ELMOEA/D. We conduct experiments with several well-known multi-objective benchmark problems and compare the proposed methods with the conventional ELMOEA/D with the fixed number of generations. The experimental results reveal that the proposed methods achieve a more stable search performance than ELMOEA/D with the fixed number of generations regardless of the target problems.

Automated summary

Research has shown that surrogate-assisted MOEAs have been proposed to reduce optimization computation time, with an automatic adjustment method for search generations in ELMOEA/D. Experimental results demonstrate that this method achieves more stable search performance than the traditional ELMOEA/D across different target problems.