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Multiobjective Multitask Optimization-Neighborhood as a Bridge for Knowledge Transfer

The implicit parallelism of a population in evolutionary algorithms (EAs) provides an ideal platform for dealing with multiple tasks simultaneously. However, little effort has been made to explore what information among different tasks can be used as valuable knowledge to help the optimization of different tasks. This article proposes a multiobjective multitask optimization (MO-MTO) EA based on decomposition with dual neighborhoods (MTEA/D-DN), in which the neighborhood is used as a bridge to achieve knowledge transfer among different tasks. In MTEA/D-DN, each subproblem not only maintains a neighborhood (internal neighborhood) within its own task based on the Euclidean distance between weight vectors but also keeps a neighborhood (external neighborhood) with the subproblems of other tasks via gray relation analysis in order to mine valuable information and communicate among tasks. The experimental studies show that our proposed algorithm outperforms five other state-of-the-art algorithms on a set of benchmark test instances and a real-world problem in steel plant.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.27 No.1 February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 155-169

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEIGHBORHOOD
DECOMPOSITION
EVOLUTIONARY ALGORITHM (EA)
MULTIOBJECTIVE MULTITASK OPTIMIZATION (MO-MTO)

Info Detail Spesifik

10.1109/TEVC.2022.3154416

Pernyataan Tanggungjawab

Xianpeng Wang, Zhiming Dong, Lixin Tang, Qingfu Zhang

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