.png){kind=icon}
返回
Preference-based multi-objective evolutionary algorithm with linear combination scalarizing function and reference point adjustment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.111296.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In practice, the decision -maker (DM) may be only interested in a particular part of Pareto optimal front (PF). For this reason, many preference -based multi -objective evolutionary algorithms (MOEA) have been proposed to find a solution set that approximates the region of the interest (ROI). Most existing preference -based methods focus on selecting solutions in the ROI. Nevertheless, the enhancement of convergence in preference -based MOEAs has been neglected. Most decomposition -based approaches often employ the achievement scalarizing function (ASF) as their scalarizing function. However, it holds a weaker search ability than the weighted sum function (WSF) despite its capability to tackle problems with arbitrary PF geometries. In order to strengthen the selective pressure toward the PF, this paper proposes a new scalarizing function, LSF, which is a linear combination of the WSF and the ASF. Then, a simple adaptive penalty scheme is employed in LSF to balance the search ability and robustness. To focus the search on the ROI, we develop a reference point adjustment method that dynamically adjusts the position of the reference point according to the distance from the approximated target point. We apply the above two innovations to the MOEA/D framework and propose a new preference -based MOEA, namely RAMOEAD. Experimental results show that RAMOEAD is highly competitive when compared with five state-of-the-art preference -based MOEAs. Finally, the proposed algorithm is extended to double reference points for solving the problems in which the ROIs are defined by the reservation and aspiration points.
Keyword:
Evolutionary multi-objective optimization
Scalarizing function
Reference point
Preference
Aspiration and reservation points
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
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