Text

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

In this paper, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on large-scale multiobjective optimization. The main idea is to track the Pareto optimal set (PS) directly via problem reformulation. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the PS. Afterwards, the original large-scale multiobjective optimization problem is reformulated into a low-dimensional single-objective optimization problem. In the reformulated problem, the decision space is reconstructed by the weight variables and the objective space is reduced by an indicator function. Thanks to the low dimensionality of the weight variables and reduced objective space, a set of quasi-optimal solutions can be obtained efficiently. Finally, a multiobjective evolutionary algorithm is used to spread the quasi-optimal solutions over the approximate Pareto optimal front evenly. Experiments have been conducted on a variety of large-scale multiobjective problems with up to 5000 decision variables. Four different types of representative algorithms are embedded into the proposed framework and compared with their original versions, respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for large-scale multiobjective optimization. The experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multiobjective optimization.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.23 Issue 6 December 2019

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 949 - 961

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EVOLUTIONARY ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
LARGE-SCALE OPTIMIZATION
PROBLEM REFORMULATION

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xin Yao

Versi lain/terkait

Lampiran Berkas

Komentar