Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts

Liu, Yiping - Nama Orang; Ishibuchi, Hisao - Nama Orang; Nojima, Yusuke - Nama Orang; Masuyama, Naoki - Nama Orang;

The performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) often deteriorates clearly when solving multiobjective optimization problems with irregular Pareto fronts (PFs). The main reason is the improper settings of reference vectors and scalarizing functions. In this paper, we propose a decomposition-based MOEA guided by a growing neural gas network, which learns the topological structure of the PF. Both reference vectors and scalarizing functions are adapted based on the topological structure to enhance the evolutionary algorithm's search ability. The proposed algorithm is compared with eight state-of-the-art optimizers on 34 test problems. The experimental results demonstrate that the proposed method is competitive in handling irregular PFs.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art135535	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.24 No.3 June 2020
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 439 - 453
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: IRREGULAR PARETO FRONT
DECOMPOSITION-BASED MULTIOBJECTIVE EVOLUTIONARY OP
GROWING NEURAL GAS (GNG)
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Yiping Liu, Hisao Ishibuchi, Naoki Masuyama, Yusuke Nojima

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