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Evolutionary Search With Multiview Prediction for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.5 October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 911-925

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

DYNAMIC MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY SEARCH
PREDICTION-BASED METHOD

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3135020

Pernyataan Tanggungjawab

Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu

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