Text

Merged Differential Grouping for Large-Scale Global Optimization

The divide-and-conquer strategy has been widely used in cooperative co-evolutionary algorithms to deal with large-scale global optimization problems, where a target problem is decomposed into a set of lower-dimensional and tractable subproblems to reduce the problem complexity. However, such a strategy usually demands a large number of function evaluations to obtain an accurate variable grouping. To address this issue, a merged differential grouping (MDG) method is proposed in this article based on the subset–subset interaction and binary search. In the proposed method, each variable is first identified as either a separable variable or a nonseparable variable. Afterward, all separable variables are put into the same subset, and the nonseparable variables are divided into multiple subsets using a binary-tree-based iterative merging method. With the proposed algorithm, the computational complexity of interaction detection is reduced to O(max{n,nns×log2k}) , where n , nns(≤n) , and k(

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.6 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1439-1451

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

PROBLEM DECOMPOSITION
COOPERATIVE CO-EVOLUTION
DIFFERENTIAL GROUPING (DG)
LARGE-SCALE GLOBAL OPTIMIZATION (LSGO)

Info Detail Spesifik

10.1109/TEVC.2022.3144684

Pernyataan Tanggungjawab

Xiaoliang Ma, Zhitao Huang, Xiaodong Li, Lei Wang, Yutao Qi, Zexuan Zhu

Versi lain/terkait

Lampiran Berkas

Komentar