CLC number: O224
On-line Access: 2022-10-26
Received: 2021-10-26
Revision Accepted: 2022-10-26
Crosschecked: 2022-04-14
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Citations: Bibtex RefMan EndNote GB/T7714
Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU. DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2100508 @article{title="DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers", %0 Journal Article TY - JOUR
DIP-MOEA:一种形式化表达决策者偏好的双重网格交互偏好多目标进化算法1国防科技大学电子对抗学院,中国合肥市,230037 2国防科技大学第三学科交叉中心,中国合肥市,230037 3中国人民解放军78092部队,中国成都市,610000 摘要:几乎所有现有的基于偏好的多目标进化算法(MOEA)给出的最终解集都与决策者偏好信息的表示存在一定距离。因此,提出一种多目标优化算法,称为双重网格交互式基于偏好的多目标进化算法(DIP-MOEA),该算法明确考虑了决策者偏好。首先根据实际多目标优化问题(MOPs)的优化目标和决策者偏好映射隶属度函数,生成决策偏好度网格和偏好误差网格。其次,提出偏好度支配和偏好误差支配两种种群支配方式,并利用该方案更新两个网格中的种群。最后综合两个网格中的种群并结合决策者偏好交互信息可进行偏好多目标优化交互。为验证DIP-MOEA性能,我们在基本DTLZ系列函数和多目标背包问题上对DIP-MOEA进行测试,并将其与几种流行的基于偏好的多目标进化算法进行比较。实验结果表明,DIP-MOEA能较好表达决策者偏好信息,提供满足决策者偏好的解集,快速求解测试问题结果,并在最终解集的Pareto前沿分布性具有较好表现。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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