.png){kind=icon}
返回
Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation
.png){kind=icon}
PRE
AI
DOI：10.1007/s12293-022-00368-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a typical large-scale multiobjective optimization problem extracted from real-world applications, the voltage transformer ratio error estimation (TREE) problem is challenging for existing evolutionary algorithms (EAs). Due to the large number of decision variables in the problems, existing algorithms cannot solve TREE problems efficiently. Besides, most EAs may fail to balance the convergence enhancement and diversity maintenance, leading to the trap in local optima even at the early stage of the evolution. This work proposes an adaptive large-scale multiobjective EA (LSMOEA) to handle the TREE problems with thousands of decision variables. Generally, multiple efficient offspring generation and environmental selection strategies selected from some representative LSMOEAs are included. Then an adaptive selection strategy is used to determine which offspring generation and environmental selection operators are used in each generation of the evolution. Thus, the search behavior of the proposed algorithm evolves along with the evolution process, the balance between convergence and diversity is maintained, and the proposed algorithm is expected to solve TREE problems effectively and efficiently. Experimental results show that the proposed algorithm achieves significant performance improvement due to the adaptive selection of different operators, providing an effective and efficient approach for large-scale optimization problems.
Keyword:
Large-scale optimization
Multiobjective optimization
Adaptive operator selection
Voltage transformer ratio error estimation
期刊
IF:
2.3
论文数:
453
被引数:
718
机构
暂无机构信息
引用论文
.png){kind=icon}
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏分享收藏分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容