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Large-Scale Evolutionary Multiobjective Optimization Assisted by Directed Sampling

It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.4 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 724 - 738

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
NONDOMINATED SORTING
REFERENCE VECTORS
DIRECTED SAMPLING (DS)
LARGE-SCALE MULTIOBJECTIVE PROBLEMS (LSMOPS)

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3063606

Pernyataan Tanggungjawab

Shufen Qin, Chaoli Sun, Yaochu Jin, Ying Tan, Jonathan Fieldsend

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