A novel global optimization method- Genetic pattern search

Yudong Zhang; Lenan Wu; Yuankai Huoc; Shuihua Wang

doi:10.4028/www.scientific.net/AMM.44-47.3240

A novel global optimization method- Genetic pattern search

Yudong Zhang, Lenan Wu^*, Yuankai Huoc, Shuihua Wang

^*Corresponding author for this work

Southeast University, Nanjing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

7 Citations (Scopus)

Abstract

A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

Original language	English
Title of host publication	Frontiers of Manufacturing and Design Science
Pages	3240-3244
Number of pages	5
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.44-47.3240
Publication status	Published - 2011
Externally published	Yes
Event	2010 International Conference on Frontiers of Manufacturing and Design Science, ICFMD2010 - Chongqing, China Duration: 11 Dec 2010 → 12 Dec 2010

Publication series

Name	Applied Mechanics and Materials
Volume	44-47
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	2010 International Conference on Frontiers of Manufacturing and Design Science, ICFMD2010
Country/Territory	China
City	Chongqing
Period	11/12/10 → 12/12/10

Keywords

Coarse search
Fine search
Genetic algorithm
Global optimization

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10.4028/www.scientific.net/AMM.44-47.3240

Cite this

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title = "A novel global optimization method- Genetic pattern search",

abstract = "A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.",

keywords = "Coarse search, Fine search, Genetic algorithm, Global optimization",

author = "Yudong Zhang and Lenan Wu and Yuankai Huoc and Shuihua Wang",

year = "2011",

doi = "10.4028/www.scientific.net/AMM.44-47.3240",

language = "English",

isbn = "9783037850046",

series = "Applied Mechanics and Materials",

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booktitle = "Frontiers of Manufacturing and Design Science",

note = "2010 International Conference on Frontiers of Manufacturing and Design Science, ICFMD2010 ; Conference date: 11-12-2010 Through 12-12-2010",

}

Zhang, Y, Wu, L, Huoc, Y & Wang, S 2011, A novel global optimization method- Genetic pattern search. in Frontiers of Manufacturing and Design Science. Applied Mechanics and Materials, vol. 44-47, pp. 3240-3244, 2010 International Conference on Frontiers of Manufacturing and Design Science, ICFMD2010, Chongqing, China, 11/12/10. https://doi.org/10.4028/www.scientific.net/AMM.44-47.3240

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AU - Zhang, Yudong

AU - Wu, Lenan

AU - Huoc, Yuankai

AU - Wang, Shuihua

PY - 2011

Y1 - 2011

N2 - A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

AB - A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

KW - Coarse search

KW - Fine search

KW - Genetic algorithm

KW - Global optimization

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Y2 - 11 December 2010 through 12 December 2010

ER -

A novel global optimization method- Genetic pattern search

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Keywords

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