Research Article
A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA
1: t:=0; | 2: Front (t):= ; | 3: Sub-Front (t):= ; | 4: Create initial population P (t) at random | 5: Calculate () P (t) | 6: Order population P (t) according to the niche count Definition14 | 7: Inclusion of the individuals of P (t) in Front (t) using Algorithm2 | 8: Inclusion of the individuals of P (t) Front (t) in Sub-Front (t) using Algorithm3 | 9: for t 1:Number of iterations | 10: Create population G (t) using Algorithm4 | 11: Calculate () G (t) | 12: Inclusion of the individuals of G (t) in Front (t) using Algorithm2 | 13: Inclusion of the individuals of G (t) Front (t) in Sub-Front (t) using Algorithm3 | 14: Update P (t) with the individuals of G (t) using Algorithm5 | 15: Order population P (t) | 16: end for |
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