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

10 Citations (Scopus)

Abstract

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
Volume6
Issue number3
DOIs
Publication statusPublished - 1 Jun 2015
MoE publication typeA1 Journal article-refereed

Keywords

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

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