A practical regularity model based evolutionary algorithm for multiobjective optimization

It is well known that domain knowledge helps design efficient problem solvers. The regularity model based multiobjective estimation of distribution algorithm (RM-MEDA) is such a method that uses the regularity property of continuous multiobjective optimization problems (MOPs). However, RM-MEDA may fail to work when dealing with complicated MOPs. This paper aims to propose some practical strategies to improve the performance of RM-MEDA. We empirically study the modeling and sampling components of RM-MEDA that influence its performance. After that, some new components, including the population partition, modeling, and offspring generation procedures, are designed and embedded in the regularity model. The experimental study suggests that the new components are more efficient than those in RM-MEDA when using the regularity model. The improved version has also been verified on various complicated benchmark problems, and the experimental results have shown that the new version outperforms five state-of-the-art multiobjective evolutionary algorithms. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for multiobjective optimization problems (MOPs). The authors empirically study and optimize the modeling and sampling components of RM-MEDA, resulting in improved performance. Experimental results demonstrate that the optimized algorithm outperforms five state-of-the-art multiobjective evolutionary algorithms on various benchmark problems.