.png){kind=icon}
返回
Variable interaction network analysis to enhance boundary update method for constrained optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.rineng.2024.103727.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The boundary update approach was proposed by Gandomi and Deb [Computer Methods in Applied Mechanics and Engineering, 363, 112,917, 2020] for constrained optimization problems. The boundary update (BU) method defines a dynamic formulation of an optimization problem in order to eliminate the infeasible search space. This study investigates the concept of variable interaction using a differential grouping algorithm, DG2, to evaluate and visualize variable interaction impacted by the boundary update method. Using multiple social network analysis (SNA) metrics, including Average Betweenness Centrality, Average Closeness Centrality, Network Density, and Clustering Coefficient, this research reveals significant structural simplifications in optimization problems under the BU method. Results show significant improvements in some aspects by applying the BU method. For example, it reduces network density by up to 34 %, closeness centrality by over 67 %, and enhances independence among variables by 45 %, simplifying the optimization landscape. Furthermore, a systematic evaluation using the TOPSIS multi-criteria decision-making (MCDM) approach confirms that BU improves convergence efficiency and solution quality by 20%-30 % compared to without BU methods across various benchmark problems. Network visualizations corroborate these findings, demonstrating reduced complexity and improved clarity in variable relationships. This comprehensive analysis establishes the BU method as a transformative framework, significantly advancing constrained optimization through its ability to streamline variable interactions and enhance algorithmic performance.
Keyword:
Boundary update
Variable interaction
Network analysis
Optimization
Evolutionary computation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.9
论文数:
1.2W
被引数:
1.7W
机构
引用论文
Metaheuristic optimization algorithms for real-world electrical and civil engineering application: A review现实世界电气和土木工程应用的元启发式优化算法: 综述OAAI
OAAI分享收藏Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative StudyOAAI
OAAI分享收藏Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization面向大规模优化的差分分组协同进化算法
OAAI
OAAI分享收藏DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box OptimizationDG2: 用于大规模黑盒优化的更快，更准确的差分分组OAAI
OAAI分享收藏A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization基于种群算法的约束处理技术综述: 从单目标到多目标优化OAAI
OAAI分享收藏Efficient implicit constraint handling approaches for constrained optimization problems.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容