.png){kind=icon}
返回
A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3131587.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary classification models to guide the evolution of the population for solving the multiobjective optimization problems. However, the regression models will make the algorithm to be increasingly computation-expensive as the number of objectives increases, while the use of the binary classification models might suffer from the poor diversity since the diversified information of solutions cannot be reflected in these classification models. For this issue, this paper proposes a surrogate-assisted many-objective evolutionary algorithm using the cooperation of the multi-classification and regression models to improve the search quality while reducing the computational cost. Our approach includes two parts: At the model training stage, a multi-classification model is constructed to divide the whole population into several classes for ensuring diversity, a distance regression model and an angle regression model are used to select solutions with better convergence and diversity in each class; At the evolution stage, a coevolutionary framework is used to guide the evolution according to a new selection criterion. Experimental results verify the effectiveness of the proposed algorithm on a set of expensive test problems with up to 10 objectives.
Keyword:
Statistics
Sociology
Optimization
Computational modeling
Evolutionary computation
Training
Linear programming
Coevolution
expensive optimization
multi-classification
surrogate-assisted evolutionary algorithm
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China
.png){kind=icon}
PREAI
分享收藏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
查看更多内容