Work

Pre-variable sorting-based differential grouping for large scale optimization: An interdependency method

Year: 2025

Type: article

Abstract: This study presents a novel pre-variable sorting-based differential grouping (Pv-DG) method for decomposing large-scale optimization problems into smaller, more manageable sub-problems. By sorting design variables according to their impact on the objective function and applying a recursive differential grouping strategy, the method effectively identifies variable interdependencies. It organizes dependent variables into cohesive groups while distributing independent variables into smaller subgroups. This approach reduces computational complexity and enhances optimization performance. Experimental results of IEEE CEC’2010 and CEC’2013 benchmark functions demonstrate that Pv-DG outperforms four state-of-the-art techniques: differential grouping DG2, fast interdependency identification and recursive differential grouping achieving higher accuracy and efficiency in variable grouping. When integrated within a cooperative co-evolutionary framework, Pv-DG further improves the overall optimization effectiveness for large-scale optimization problems.

Source: AIP Advances

DOI: 10.1063/5.0283612

Authors: A. Hussain, Z. Tang

Institution: Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,350

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities