Gravitational Co-evolution and Opposition-based Optimization Algorithm

Yang LOU*, Junli LI, Yuhui SHI, Linpeng JIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

6 Citations (Scopus)


In this paper, a Gravitational Co-evolution and Opposition-based Optimization (GCOO) algorithm is proposed for solving unconstrained optimization problems. Firstly, under the framework of gravitation based co-evolution, individuals of the population are divided into two subpopulations according to their fitness values (objective function values), i.e., the elitist subpopulation and the common subpopulation, and then three types of gravitation-based update methods are implemented. With the cooperation of opposition-based operation, the proposed algorithm conducts the optimizing process collaboratively. Three benchmark algorithms and fifteen typical benchmark functions are utilized to evaluate the performance of GCOO, where the substantial experimental data shows that the proposed algorithm has better performance with regards to effectiveness and robustness in solving unconstrained optimization problems.

Original languageEnglish
Pages (from-to)849-861
Number of pages13
JournalInternational Journal of Computational Intelligence Systems
Issue number5
Early online date1 Sep 2013
Publication statusPublished - Sep 2013
Externally publishedYes

Bibliographical note

Funding Information:
This paper is partially supported by National Natural Science Foundation of China under Grant Numbers 60975080, 61273367, 60832003; Natural Science Foundation of Ningbo under Grant No.2012A610047; and Sichuan Science and Technology Support Plan


  • Co-evolution
  • Evolution algorithm
  • Gravitation
  • Opposition-based
  • Optimization


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