Evolutionary Multi-objective Optimisation by Diversity Control

  • Pasan Kulvanit
  • Theera Piroonratana
  • Nachol Chaiyaratana
  • Djitt Laowattana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3967)


This paper presents an improved multi-objective diversity control oriented genetic algorithm (MODCGA-II). The performance comparison between the MODCGA-II, a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto evolutionary algorithm (SPEA-II) is carried out where different two- and three-objective benchmark problems with specific multi-objective characteristics are used. The results indicate that the two-objective MODCGA-II solutions are better than the solutions generated by the NSGA-II and SPEA-II in terms of the closeness to the true Pareto optimal solutions and the uniformity of solution distribution along the Pareto front. In contrast, the NSGA-II in overall produces the best solutions in three-objective problems. As a result, the limitation of the proposed algorithm is identified.


Genetic Algorithm Pareto Front Multiobjective Optimization True Pareto Front DTLZ6 Problem 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pasan Kulvanit
    • 1
  • Theera Piroonratana
    • 2
  • Nachol Chaiyaratana
    • 2
  • Djitt Laowattana
    • 1
  1. 1.Institute of Field RoboticsKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.Research and Development Center for Intelligent SystemsKing Mongkut’s Institute of Technology North BangkokBangkokThailand

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