Research Article

A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Algorithm 1

Main pseudocode
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