High-Performance Architecture for the Modified NSGA-II

  • Josué Domínguez
  • Oscar Montiel-Ross
  • Roberto Sepúlveda
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 294)


NSGA-II is one of the most popular algorithms for solving Multiobjective Optimization Problems. It has been used to solve different real-world optimization problems; however, NSGA-II has been criticized for its high computational cost and bad performance on applications with more than two objective functions. In this paper, we propose a high-performance architecture for the NSGA-II using parallel computing, for evaluation functions and genetic operators. In the proposed architecture, the Mishra Fast Algorithm for finding the Non Dominated Set was used. In this paper, we propose a modification in the sorting process for the NSGA-II that improves the distribution of the solutions in the Pareto front. Results for five different test functions using distinct crossover and mutation operators to test performance are presented.


Genetic Algorithm Multi-Objective Optimization Pareto Optimal NSGA – II 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Josué Domínguez
    • 1
  • Oscar Montiel-Ross
    • 1
  • Roberto Sepúlveda
    • 1
  1. 1.Instituto Politécnico Nacional – CITEDITijuanaMéxico

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