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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

 ORCID:

Luda ZHAO

https://orcid.org/0000-0002-7476-5896

Bin WANG

https://orcid.org/0000-0003-0593-3531

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.11 P.1714-1732

http://doi.org/10.1631/FITEE.2100508


DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers


Author(s):  Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU

Affiliation(s):  College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; more

Corresponding email(s):   zhaoluda@nudt.edu.cn, wbeeinudt@126.com

Key Words:  Multi-objective evolutionary algorithm (MOEA), Formalizing preference of decision makers, Population renewal strategy, Preference interaction


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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, 2022, 23(11): 1714-1732.

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Abstract: 
The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.

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

赵禄达1,2,王斌1,2,姜晓平1,2,卢义成3,胡以华1,2
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:多目标进化算法(MOEA);决策者偏好形式化;种群更新策略;偏好交互

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