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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

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Mingtao DONG


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


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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author="Mingtao DONG, Jianhua CHENG, Lin ZHAO",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T A combination weighting model based on iMOEA/D-DE
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000545

T1 - A combination weighting model based on iMOEA/D-DE
A1 - Mingtao DONG
A1 - Jianhua CHENG
A1 - Lin ZHAO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
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SP - 604
EP - 616
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Y1 - 2022
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000545

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.




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


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