A new grouping strategy-based hybrid algorithm for large scale global optimization problems

Haiyan Liu; Yuping Wang; Liwen Liu; Xiao-Zhi Gao; Yiu-ming Cheung

doi:10.1145/3067695.3075978

A new grouping strategy-based hybrid algorithm for large scale global optimization problems

Haiyan Liu, Yuping Wang^*, Liwen Liu, Xiao-Zhi Gao, Yiu-ming Cheung

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Large scale global optimization (LSGO) problems are a kind of very challenging problems due to their high nonlinearity, high dimensionality and too many local optimal solutions. The variable grouping strategies including black-box grouping strategies and white-box grouping strategy are the most hopeful strategies which can decompose a large scale problem into several smaller scale sub-problems and make the problem solving become easier. In this paper, we first propose a new variable grouping strategy which can be applicable to fully non-separable LSGO problems. Then, a new line search method is designed which can make a quick scan to arrive in promising regions and help the new variable grouping strategy to divide the LSGO problem properly. Furthermore, a differential evolutionary (DE) algorithm with a new mutation strategy is designed. Combining all these, a new hybrid algorithm for LSGO problems is proposed.

Original language	English
Title of host publication	Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	171–172
Number of pages	2
ISBN (Print)	9781450349390
DOIs	https://doi.org/10.1145/3067695.3075978
Publication status	Published - 15 Jul 2017
Event	Genetic and Evolutionary Computation Conference, GECCO 2017 - Berlin, Germany Duration: 15 Jul 2017 → 19 Jul 2017 https://dl.acm.org/doi/proceedings/10.1145/3067695

Publication series

Name	GECCO '17
Publisher	Association for Computing Machinery

Conference

Conference	Genetic and Evolutionary Computation Conference, GECCO 2017
Country/Territory	Germany
City	Berlin
Period	15/07/17 → 19/07/17
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3067695

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/3067695.3075978

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@inproceedings{6a6e42b71228465ca68c48368ae58139,

title = "A new grouping strategy-based hybrid algorithm for large scale global optimization problems",

abstract = "Large scale global optimization (LSGO) problems are a kind of very challenging problems due to their high nonlinearity, high dimensionality and too many local optimal solutions. The variable grouping strategies including black-box grouping strategies and white-box grouping strategy are the most hopeful strategies which can decompose a large scale problem into several smaller scale sub-problems and make the problem solving become easier. In this paper, we first propose a new variable grouping strategy which can be applicable to fully non-separable LSGO problems. Then, a new line search method is designed which can make a quick scan to arrive in promising regions and help the new variable grouping strategy to divide the LSGO problem properly. Furthermore, a differential evolutionary (DE) algorithm with a new mutation strategy is designed. Combining all these, a new hybrid algorithm for LSGO problems is proposed.",

author = "Haiyan Liu and Yuping Wang and Liwen Liu and Xiao-Zhi Gao and Yiu-ming Cheung",

note = "Publisher copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; Genetic and Evolutionary Computation Conference, GECCO 2017 ; Conference date: 15-07-2017 Through 19-07-2017",

year = "2017",

month = jul,

day = "15",

doi = "10.1145/3067695.3075978",

language = "English",

isbn = "9781450349390",

series = "GECCO '17",

publisher = "Association for Computing Machinery (ACM)",

pages = "171–172",

booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3067695",

}

Liu, H, Wang, Y, Liu, L, Gao, X-Z & Cheung, Y 2017, A new grouping strategy-based hybrid algorithm for large scale global optimization problems. in Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017. GECCO '17, Association for Computing Machinery (ACM), New York, pp. 171–172, Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, 15/07/17. https://doi.org/10.1145/3067695.3075978

A new grouping strategy-based hybrid algorithm for large scale global optimization problems. / Liu, Haiyan; Wang, Yuping; Liu, Liwen et al.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017. New York: Association for Computing Machinery (ACM), 2017. p. 171–172 (GECCO '17).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A new grouping strategy-based hybrid algorithm for large scale global optimization problems

AU - Liu, Haiyan

AU - Wang, Yuping

AU - Liu, Liwen

AU - Gao, Xiao-Zhi

AU - Cheung, Yiu-ming

PY - 2017/7/15

Y1 - 2017/7/15

N2 - Large scale global optimization (LSGO) problems are a kind of very challenging problems due to their high nonlinearity, high dimensionality and too many local optimal solutions. The variable grouping strategies including black-box grouping strategies and white-box grouping strategy are the most hopeful strategies which can decompose a large scale problem into several smaller scale sub-problems and make the problem solving become easier. In this paper, we first propose a new variable grouping strategy which can be applicable to fully non-separable LSGO problems. Then, a new line search method is designed which can make a quick scan to arrive in promising regions and help the new variable grouping strategy to divide the LSGO problem properly. Furthermore, a differential evolutionary (DE) algorithm with a new mutation strategy is designed. Combining all these, a new hybrid algorithm for LSGO problems is proposed.

AB - Large scale global optimization (LSGO) problems are a kind of very challenging problems due to their high nonlinearity, high dimensionality and too many local optimal solutions. The variable grouping strategies including black-box grouping strategies and white-box grouping strategy are the most hopeful strategies which can decompose a large scale problem into several smaller scale sub-problems and make the problem solving become easier. In this paper, we first propose a new variable grouping strategy which can be applicable to fully non-separable LSGO problems. Then, a new line search method is designed which can make a quick scan to arrive in promising regions and help the new variable grouping strategy to divide the LSGO problem properly. Furthermore, a differential evolutionary (DE) algorithm with a new mutation strategy is designed. Combining all these, a new hybrid algorithm for LSGO problems is proposed.

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DO - 10.1145/3067695.3075978

M3 - Conference proceeding

SN - 9781450349390

T3 - GECCO '17

SP - 171

EP - 172

BT - Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - Genetic and Evolutionary Computation Conference, GECCO 2017

Y2 - 15 July 2017 through 19 July 2017

ER -

A new grouping strategy-based hybrid algorithm for large scale global optimization problems

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