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 proceedingConference contribution

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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 languageEnglish
Title of host publicationThe 2017 4th International Conference on Systems and Informatics (ICSAI 2017)
PublisherIEEE Press
Pages1328-1333
Number of pages6
ISBN (Print)978-1-5386-1106-7
Publication statusPublished - 2017
EventThe 2017 4th International Conference on Systems and Informatics - Hangzhou Zhejiang, China
Duration: 11 Nov 201713 Nov 2017

Conference

ConferenceThe 2017 4th International Conference on Systems and Informatics
Abbreviated titleICSAI 2017
CountryChina
CityHangzhou Zhejiang
Period11/11/1713/11/17

Fingerprint

astronomy
heuristics
genetic algorithm
planet
detection
comparison
particle

Keywords

  • gravitation field algorithm
  • optimal detection
  • unconstraint optimization

Cite this

Huang, L., Hu, X., Wang, Y., Zhang, F., Liu , Z., & Pang, W. (2017). Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. In The 2017 4th International Conference on Systems and Informatics (ICSAI 2017) (pp. 1328-1333). IEEE Press.

Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. / Huang, Lan; Hu, Xuemei; Wang, Yan; Zhang, Fang; Liu , Zhendong ; Pang, Wei.

The 2017 4th International Conference on Systems and Informatics (ICSAI 2017). IEEE Press, 2017. p. 1328-1333.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, L, Hu, X, Wang, Y, Zhang, F, Liu , Z & Pang, W 2017, Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. in The 2017 4th International Conference on Systems and Informatics (ICSAI 2017). IEEE Press, pp. 1328-1333, The 2017 4th International Conference on Systems and Informatics , Hangzhou Zhejiang, China, 11/11/17.
Huang L, Hu X, Wang Y, Zhang F, Liu Z, Pang W. Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. In The 2017 4th International Conference on Systems and Informatics (ICSAI 2017). IEEE Press. 2017. p. 1328-1333
Huang, Lan ; Hu, Xuemei ; Wang, Yan ; Zhang, Fang ; Liu , Zhendong ; Pang, Wei. / Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. The 2017 4th International Conference on Systems and Informatics (ICSAI 2017). IEEE Press, 2017. pp. 1328-1333
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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).",
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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).

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