Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization

Lan Huang; Xuemei Hu; Yan Wang; Fang Zhang; Zhendong  Liu; Wei Pang

Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization

Lan Huang, Xuemei Hu, Yan Wang, Fang Zhang, Zhendong Liu , Wei Pang

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

2 Citations (Scopus)

13 Downloads (Pure)

Abstract

Gravitation field algorithm (GFA) is a novel
optimization algorithm derived from the Solar Nebular Disk
Model (SNDM) in astronomy, based on the formation of planets,
in recent years. In this research, an improved GFA with Optimal
Detection (GFA-OD) is proposed for unconstrained optimization
problems. Optimal Detection can efficiently locate the space that
more likely contains the optimal solution(s) by initializing part of
dust population randomly in the search space of a given problem,
and then improves the accuracy of solutions. The comparison of
results on four classical unconstrained optimization problems
with varying dimensions demonstrates that the proposed GFAOD
outperforms many other classical heuristic optimization
algorithms in accuracy, efficiency and running time in lower
dimensions, such as Genetic Algorithm (GA) and Particle Swarm
Optimization (PSO).

Original language	English
Title of host publication	The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)
Publisher	IEEE Press
Pages	1328-1333
Number of pages	6
ISBN (Print)	978-1-5386-1106-7
Publication status	Published - 2017
Event	The 2017 4th International Conference on Systems and Informatics - Hangzhou Zhejiang, China Duration: 11 Nov 2017 → 13 Nov 2017

Conference

Conference	The 2017 4th International Conference on Systems and Informatics
Abbreviated title	ICSAI 2017
Country/Territory	China
City	Hangzhou Zhejiang
Period	11/11/17 → 13/11/17

Bibliographical note

This work is supported by the National Natural Science Foundation of
China (Grant Nos. 61472159, 61572227), Development Project of Jilin
Province of China (Nos. 20160204022GX, 20160414009GH, 2017C033).

Keywords

gravitation field algorithm
optimal detection
unconstraint optimization

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ICSAI 20171023hxm_finalaccepted
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Accepted author manuscript, 1.37 MBLicence: Other

Cite this

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title = "Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization",

abstract = "Gravitation field algorithm (GFA) is a noveloptimization algorithm derived from the Solar Nebular DiskModel (SNDM) in astronomy, based on the formation of planets,in recent years. In this research, an improved GFA with OptimalDetection (GFA-OD) is proposed for unconstrained optimizationproblems. Optimal Detection can efficiently locate the space thatmore likely contains the optimal solution(s) by initializing part ofdust population randomly in the search space of a given problem,and then improves the accuracy of solutions. The comparison ofresults on four classical unconstrained optimization problemswith varying dimensions demonstrates that the proposed GFAODoutperforms many other classical heuristic optimizationalgorithms in accuracy, efficiency and running time in lowerdimensions, such as Genetic Algorithm (GA) and Particle SwarmOptimization (PSO). ",

keywords = "gravitation field algorithm, optimal detection , unconstraint optimization",

author = "Lan Huang and Xuemei Hu and Yan Wang and Fang Zhang and Zhendong Liu and Wei Pang",

note = "This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472159, 61572227), Development Project of Jilin Province of China (Nos. 20160204022GX, 20160414009GH, 2017C033). ; The 2017 4th International Conference on Systems and Informatics , ICSAI 2017 ; Conference date: 11-11-2017 Through 13-11-2017",

year = "2017",

language = "English",

isbn = "978-1-5386-1106-7",

pages = "1328--1333",

booktitle = "The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)",

publisher = "IEEE Press",

}

TY - GEN

T1 - Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization

AU - Huang, Lan

AU - Hu, Xuemei

AU - Wang, Yan

AU - Zhang, Fang

AU - Liu , Zhendong

AU - Pang, Wei

N1 - This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472159, 61572227), Development Project of Jilin Province of China (Nos. 20160204022GX, 20160414009GH, 2017C033).

PY - 2017

Y1 - 2017

N2 - Gravitation field algorithm (GFA) is a noveloptimization algorithm derived from the Solar Nebular DiskModel (SNDM) in astronomy, based on the formation of planets,in recent years. In this research, an improved GFA with OptimalDetection (GFA-OD) is proposed for unconstrained optimizationproblems. Optimal Detection can efficiently locate the space thatmore likely contains the optimal solution(s) by initializing part ofdust population randomly in the search space of a given problem,and then improves the accuracy of solutions. The comparison ofresults on four classical unconstrained optimization problemswith varying dimensions demonstrates that the proposed GFAODoutperforms many other classical heuristic optimizationalgorithms in accuracy, efficiency and running time in lowerdimensions, such as Genetic Algorithm (GA) and Particle SwarmOptimization (PSO).

AB - Gravitation field algorithm (GFA) is a noveloptimization algorithm derived from the Solar Nebular DiskModel (SNDM) in astronomy, based on the formation of planets,in recent years. In this research, an improved GFA with OptimalDetection (GFA-OD) is proposed for unconstrained optimizationproblems. Optimal Detection can efficiently locate the space thatmore likely contains the optimal solution(s) by initializing part ofdust population randomly in the search space of a given problem,and then improves the accuracy of solutions. The comparison ofresults on four classical unconstrained optimization problemswith varying dimensions demonstrates that the proposed GFAODoutperforms many other classical heuristic optimizationalgorithms in accuracy, efficiency and running time in lowerdimensions, such as Genetic Algorithm (GA) and Particle SwarmOptimization (PSO).

KW - gravitation field algorithm

KW - optimal detection

KW - unconstraint optimization

M3 - Published conference contribution

SN - 978-1-5386-1106-7

SP - 1328

EP - 1333

BT - The 2017 4th International Conference on Systems and Informatics (ICSAI 2017)

PB - IEEE Press

T2 - The 2017 4th International Conference on Systems and Informatics

Y2 - 11 November 2017 through 13 November 2017

ER -

Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization

Abstract

Conference

Bibliographical note

Keywords

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