.png){kind=icon}
返回
A novel preference-driven evolutionary algorithm for dynamic multi-objective problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101638.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.
Keyword:
Dynamic multi-objective optimization
Evolutionary algorithm
Preference information
Reference points
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons进化动态多目标优化: 基准和算法比较
PREAI
分享收藏A multi-objective optimization framework for energy and asset management in an industrial Microgrid
PREAI
分享收藏Multi-objective scheduling of dynamic job shop using variable neighborhood search基于变邻域搜索的动态作业车间多目标调度
PREAI
分享收藏A preference-based multi-objective evolutionary algorithm using preference selection radius.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容