A Two-Archive Algorithm with Interactive Mechanism for Many-Objective Optimization

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2023, Vol.2023 (1)
Main Authors: Liu, Shuren, Li, Shuping, Li, Changchun, Cai, Changning, Cheng, Guojian
Format: Article
Language:English
Subjects:
Archives & records
Convergence
Evolutionary algorithms
Genetic algorithms
Methods
Multiple objective analysis
Objectives
Optimization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization.
ISSN:1024-123X
1563-5147
DOI:10.1155/2023/2005465