Work

A Group Rotate-Vector Algorithm for Mixed-Variable Optimization Problems

Year: 2024

Type: article

Abstract: Many engineering design optimization problems can be represented as mixed-variable optimization problems. This study presents a heuristic approach for solving mixed-variable optimization using rotation and contraction of vectors. The current optimization algorithm, known as rotate-vector, has shown promising results in solving optimization problems with real or integer variables. Building upon this concept, the authors of this paper develop methods to solve 0-1 type and general discrete type variables, and introduce corresponding rotation and contraction operators. To address the simultaneous search and optimization of multiple variable types in nonlinear mixed integer programming problems, a grouping and comprehensive processing method is employed. The proposed group rotate-vector algorithm (GRV) is evaluated through the solution of multiple 0-1 programming problems and mixed-variable optimization problems. The study also investigates the impact of parameter settings on solution quality and efficiency. The experimental results demonstrate that the GRV algorithm outperforms other algorithms in terms of solution quality.

Source: IEEE Access

DOI: 10.1109/access.2024.3511671

Authors: Zhongming Li, Lutao Yan, Haiyuan Li, Lianxin Wang

Institutions: Beijing University of Posts and Telecommunications, China Academy of Chinese Medical Sciences

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,995

Funder: National Natural Science Foundation of China