Text

An Evolutionary Multiobjective Framework for Complex Network Reconstruction Using Community Structure

The problem of inferring nonlinear and complex dynamical systems from available data is prominent in many fields, including engineering, biological, social, physical, and computer sciences. Many evolutionary algorithm (EA)-based network reconstruction methods have been proposed to address this problem, but they ignore several useful information of network structure, such as community structure, which widely exists in various complex networks. Inspired by the community structure, this article develops a community-based evolutionary multiobjective network reconstruction framework to promote the reconstruction performance of EA-based network reconstruction methods due to their good performance; we refer this framework as CEMO-NR. CEMO-NR is a generic framework and any population-based multiobjective metaheuristic algorithm can be employed as the base optimizer. CEMO-NR employs the community structure of networks to divide the original decision space into multiple small decision spaces, and then any multiobjective EA (MOEA) can be used to search for improved solutions in the reduced decision space. To verify the performance of CEMO-NR, this article also designs a test suite for complex network reconstruction problems. Three representative MOEAs are embedded into CEMO-NR and compared with their original versions, respectively. The experimental results have demonstrated the significant improvement benefiting from the proposed CEMO-NR in 30 multiobjective network reconstruction problems (MONRPs).

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.2 April 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 247 - 261

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY ALGORITHM (EA)
LARGE-SCALE OPTIMIZATION
COMMUNITY STRUCTURE
COMPLEX NETWORK RECONSTRUCTION

Info Detail Spesifik

DOI: 10.1109/TEVC.2020.3020423

Pernyataan Tanggungjawab

Kai Wu, Jing Liu, Xingxing Hao, Penghui Liu, Fang Shen

Versi lain/terkait

Lampiran Berkas

Komentar