Full Text:   <2017>

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CLC number: TP18;TN967.2

On-line Access: 2022-04-21

Received: 2020-10-13

Revision Accepted: 2021-02-19

Crosschecked: 2022-05-04

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Mingtao DONG

https://orcid.org/0000-0002-9929-5210

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.604-616

http://doi.org/10.1631/FITEE.2000545


A combination weighting model based on iMOEA/D-DE


Author(s):  Mingtao DONG, Jianhua CHENG, Lin ZHAO

Affiliation(s):  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   hbdmt@hrbeu.edu.cn, chengjianhua@hrbeu.edu.cn

Key Words:  Combination weighting, MOEA/D-DE, Game theory, Self-learning ability, Relative entropy


Mingtao DONG, Jianhua CHENG, Lin ZHAO. A combination weighting model based on iMOEA/D-DE[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 604-616.

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Abstract: 
This paper proposes a combination weighting (CW) model based on iMOEA/D-DE (i.‍e., improved multiobjective evolutionary algorithm based on decomposition with differential evolution) with the aim to accurately compute the weight of evaluation methods. Multi-expert weight considers only subjective weights, leading to poor objectivity. To overcome this shortcoming, a multiobjective optimization model of CW based on improved game theory is proposed while considering the uncertainty of combination coefficients. An improved mutation operator is introduced to improve the convergence speed, and thus better optimization results are obtained. Meanwhile, an adaptive mutation constant and crossover probability constant with self-learning ability are proposed to improve the robustness of MOEA/D-DE. Since the existing weight evaluation approaches cannot evaluate weights separately, a new weight evaluation approach based on relative entropy is presented. Taking the evaluation method of integrated navigation systems as an example, certain experiments are carried out. It is proved that the proposed algorithm is effective and has excellent performance.

基于iMOEA/D-DE的组合权重模型

董铭涛,程建华,赵琳
哈尔滨工程大学智能科学与工程学院,中国哈尔滨市,150001
摘要:为准确求解评估方法的权重,提出一种基于iMOEA/D-DE(基于差分进化分解的改进多目标进化算法)的组合权重模型。多专家权重仅考虑主观权重,导致客观性差。为解决此问题,考虑组合系数的不确定性,设计了基于改进博弈论的组合权重多目标优化模型。引入改进变异算子提高收敛速度,进而获得更好优化结果。同时,设计了具有自学习能力的自适应变异系数和交叉概率系数,以提高MOEA/D-DE算法的鲁棒性。由于现有权重评价方法不能单独评价权重,提出一种基于相对熵的新权重评价方法。以组合导航系统评估方法为例开展实验。实验证明,该算法具有良好性能。

关键词:组合权重;MOEA/D-DE;博弈论;自学习能力;相对熵

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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