In this section, we pose a capacity game.
Next, in Section 5, the capacity game is solved.
Hence, the capacity game can be accurately approached as a standard pursuit-evasion one and be efficiently solved.
The solution to the capacity game involves that the jammer tries to be close to the communicator and the communicator tries to be far away from the jammer.
In this work, we deepen the theoretical bases for our approach and we also solve the capacity game posed without using the surrogate function approach.
It is important to note that this game requires more iterations than the one in Table 1, and hence, the computational cost and time to solve this capacity game increases with respect to the one in the previous section.
In order to extend the approach proposed in Section 4.5 to this capacity game, the same grid used there for the initial conditions will be used here, that is, x e,0,y e,0∈{1,6,11}, x p,0,y p,0,y−10,−5,0}.
Table 2 Results obtained using optimization approach, with and without (hat {Delta v}) approximation, for capacity game Grid points where solution was found Percent Optimization approach 103 iterations 5 6.2 104 iterations 7 8.7 105 iterations 33 40.7 Optimization approach, (hat {Delta v}) approximation 103 iterations 11 13.6 104 iterations 44 54.3 105 iterations 73 90.1.
4], will be used to solve the linear approximation of the capacity game described in Section 3. The running cost L will be considered to be linear Eq. (24): L=A+B r where r= y c −y j )2+(x c −x j )2, A and B are constants whose expressions are in Eq. (25).
Stand closures and a heavily reduced capacity for games would also be necessary during the rebuild.
