.png){kind=icon}
返回
An ensemble learning-based multi-population evolutionary framework for multi-scenario multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2023.110708.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-scenario multi-objective optimization problems (MSMOPs) are a topic of considerable interest in the field of optimization. The MSMOP contains a set of multi-objective optimization problems arising from varying operating conditions, with the goal of determining a set of communal compromise solutions. However, there are few universal methods available for MSMOPs. This paper presents a general method incorporating transfer learning for MSMOPs. First, a multi-scenario ensemble framework that transfers knowledge between scenarios is developed to combine arbitrary multi-objective evolutionary algorithms, where a scenario-based comprehensive evaluation indicator is developed for combination with base learners. Then, an adaptive decomposition-based multi-objective evolutionary algorithm with a bi-layer selection (EADaBS) is proposed and embedded within the framework as a base learner. EADaBS incorporates an adaptive fitness assignment in its first layer to facilitate exploration, and density measurement in its second layer to ensure exploitation. A rebalancing operator is also designed to aid the population towards the Pareto front. Finally, a multitude of experiments is conducted to verify the effectiveness and efficiency of the proposed algorithms. Two three-scenario multiobjective optimization problems are designed and utilized as test problems. The experimental results clearly demonstrate that the proposed framework outperforms existing state-of-the-art algorithms. In conclusion, this research provides new insights into the solution of MSMOPs.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Multi -scenario optimization problem
Evolutionary algorithm
Bi-compromise task
Ensemble learning
Bi-layer selection
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Multi-scenario microgrid optimization using an evolutionary multi-objective algorithm基于进化多目标算法的多场景微电网优化
PREAI
分享收藏Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system.png)
RENEWABLE ENERGY
OAAI
RENEWABLE ENERGYIF9.1
OAAI分享收藏A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization ProblemsOAAI
OAAI分享收藏
查看更多内容