MCGA: A multiobjective cellular genetic algorithm based on a 3D grid

  • Hu Zhang
  • , Shenming Song*
  • , Aimin Zhou
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper proposes a new cellular multiobjective genetic algorithm based on a 3D grid structure. The basic idea is to organize the candidate solutions by a 3D grid, and the reproduction and replacement operators are based on the 3D grid. The proposed algorithm is compared with two 2D cellular multiobjective genetic algorithms on the DTLZ test suite, and the statistical results indicate that our approach performs better than the compared algorithms according to both the diversity and convergence metrics. Furthermore, our approach is computational more stable.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 14th International Conference, IDEAL 2013, Proceedings
Pages455-462
Number of pages8
DOIs
StatePublished - 2013
Event14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013 - Hefei, China
Duration: 20 Oct 201323 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8206 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013
Country/TerritoryChina
CityHefei
Period20/10/1323/10/13

Keywords

  • 3D grid
  • cellular genetic algorithm
  • multiobjective optimization

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