Full Text:   <1438>

CLC number: 

On-line Access: 2021-04-21

Received: 2020-10-13

Revision Accepted: 2021-02-19

Crosschecked: 0000-00-00

Cited: 0

Clicked: 2428

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

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


A CW 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 weight, MOEA/D-DE, Game theory, Self-learning ability, Relative entropy


Mingtao DONG, Jianhua CHENG, Lin ZHAO. A CW model based on iMOEA/D-DE[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

@article{title="A CW model based on iMOEA/D-DE",
author="Mingtao DONG, Jianhua CHENG, Lin ZHAO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000545"
}

%0 Journal Article
%T A CW model based on iMOEA/D-DE
%A Mingtao DONG
%A Jianhua CHENG
%A Lin ZHAO
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000545

TY - JOUR
T1 - A CW model based on iMOEA/D-DE
A1 - Mingtao DONG
A1 - Jianhua CHENG
A1 - Lin ZHAO
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000545


Abstract: 
This paper proposes a combination weight (CW) model based on iMOEA/D-DE (improved multiobjective evolutionary algorithm based on decomposition with differential evolution) with the aim to accurately compute the weight of evaluation methods. Multi-expert weight only considers subjective weights, thus leading to poor objectivity. In order 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, 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 concluded that the proposed algorithm proves to be effective and has excellent performance.

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

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE