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

2 Citations (Scopus)
28 Downloads (Pure)


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 GFA-OD 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 publication2017 4th International Conference on Systems and Informatics (ICSAI)
Number of pages6
ISBN (Electronic)9781538611074
Publication statusPublished - 8 Jan 2018


  • gravitation field algorithm
  • optimal detection
  • unconstraint optimization


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