A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

Hu Zhang, Shenmin Song*, Aimin Zhou, X. Z. Gao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)


Multiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosine crowding measurement. The first component is used to organize the population. Compared with a 2D grid, the 3D grid offers a vertical expansion of cells. The second one simultaneously considers the crowding distances and location distributions for measuring the crowding degree values for the solutions. The simulation results show that C-MCGA outperforms two typical MOcGAs and two state-of-the-art algorithms, NSGA-II and SPEA2, on a given set of test instances. Furthermore, the proposed measurement metric is compared with that in NSGA-II, which is demonstrated to yield a more diverse population on most of the test instances.

Original languageEnglish
Pages (from-to)487-500
Number of pages14
JournalInternational Journal of Machine Learning and Cybernetics
Issue number3
Publication statusPublished - 1 Jun 2015
MoE publication typeA1 Journal article-refereed


  • 3D grid
  • Cellular genetic algorithm
  • Crowding measurement
  • Multiobjective optimization


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