Decomposition-based multiobjective optimization with bicriteria assisted adaptive operator selection

Wu Lin

doi:10.1016/j.swevo.2020.100790

Decomposition-based multiobjective optimization with bicriteria assisted adaptive operator selection

2021-02-01

15

PRE

AI

Carlos A. Coello Coello

K

Ka‐Chun Wong

DOI：10.1016/j.swevo.2020.100790

Original

Original request for help

Save

Abstract

En 中文

This paper proposes a novel bicriteria assisted adaptive operator selection (B-AOS) strategy for decomposition-based multiobjective evolutionary algorithms (MOEA/Ds). In this approach, two operator pools are employed to focus on exploitation and exploration, each of which includes two DE operators with distinct search patterns. Then, two criteria, one (called the Pareto criterion) emphasizing convergence and the other (called the crowding criterion) focusing on diversity, are collaboratively used to assist the selection of a suitable DE operator for the current solution, which aims to obtain a good balance between exploitation and exploration during the evolutionary search of each solution. Specifically, the Pareto criterion is used to decide whether exploration or exploitation is preferred for the current solution, which will help to select an operator pool. After that, from the selected operator pool, the crowding criterion is used to further assist the selection of the DE operator based on a binary tournament strategy. The experimental results show that our proposed B-AOS performs better than other existing state-of-the-art adaptive operator selection methods, and several MOEA/Ds embedded with B-AOS can significantly improve their performance on most of the benchmark problems adopted.

Keywords:

Multiobjective optimization

Recombination operator

Adaptive operator selection

Decomposition-based multiobjective optimization with bicriteria assisted adaptive operator selection

Abstract

AI Summary

Journal

Organization