A dynamic neighborhood learning-based gravitational search algorithm

Aizhu Zhang, Genyun Sun, Jinchang Ren, Xiaodong Li, Zhenjie Wang, Xiuping Jia

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)
170 Downloads (Pure)


Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the Kbest model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.
Original languageEnglish
Pages (from-to)436-447
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number1
Early online date30 Dec 2016
Publication statusPublished - 30 Jan 2018


  • convergence criterion
  • dynamic neighborhood
  • evolutionary states
  • gravitational search algorithm (GSA)
  • topology

Fingerprint Dive into the research topics of 'A dynamic neighborhood learning-based gravitational search algorithm'. Together they form a unique fingerprint.

  • Genyun Sun

    Jinchang Ren (Host)

    Dec 2016Dec 2017

    Activity: Hosting a visitor typesHosting an academic visitor

Cite this