Abstract
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.
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Domínguez, J., Montiel-Ross, O., Sepúlveda, R. (2013). High-Performance Architecture for the Modified NSGA-II. In: Melin, P., Castillo, O. (eds) Soft Computing Applications in Optimization, Control, and Recognition. Studies in Fuzziness and Soft Computing, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35323-9_13
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DOI: https://doi.org/10.1007/978-3-642-35323-9_13
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