A combination weighting model based on iMOEA/D-DE

Mingtao DONG; Jianhua CHENG; Lin ZHAO

doi:10.1631/FITEE.2000545

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A combination weighting model based on iMOEA/D-DE

Regular Papers | Updated：2022-05-19

- A combination weighting model based on iMOEA/D-DE
  Enhanced Publication
- 基于iMOEA/D-DE的组合权重模型
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 4, Pages: 604-616(2022)
- Affiliations：
  
  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin150001, China
- Author bio：
  
  E-mail: hbdmt@hrbeu.edu.cn;
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2000545
  CLC： TP18;TN967.2
- Received：13 October 2020，
  
  Revised：2021-02-19，
  
  Published Online：01 April 2022，
  
  Published：2022-04
- Accepted：
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DONG Mingtao,CHENG Jianhua,ZHAO Lin.A combination weighting model based on iMOEA/D-DE[J].Frontiers of Information Technology & Electronic Engineering,2022,23(04):604-616.
DOI：

DONG Mingtao,CHENG Jianhua,ZHAO Lin.A combination weighting model based on iMOEA/D-DE[J].Frontiers of Information Technology & Electronic Engineering,2022,23(04):604-616. DOI： 10.1631/FITEE.2000545.

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。

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.

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Keywords

references

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