One often hears that the US shutdown is the result of partisan bickering, that politicians should learn to rise above it and find bipartisan solutions for the good of the nation.
Starting the inner loop of the SA with a good initial solution drives the algorithm to search for a better solution in the neighbouring area of the good solution.
The intensification strategy drives the algorithm to search for a better solution in the neighbouring area of the good solution.
Results obtained are compared with numerical computations based on an exact solution of the field, showing very good agreement within the near field.
The approach to solution of allotment problems as multicriteria public choice problems lies in the basis of this mechanism, that allows to implement Groves-Ledyard mechanism, which was initially designated to the solution of public good problems.
Using the parameters thus obtained, a numerical solution of the problem is in good agreement with the experimental data.
But the rules for constructing the sequence of graphs could get very complex, and proving that the solution of the simplest was a good approximation of the most complex required considerable mathematical ingenuity.
These results were then compared with a numerical solution of the original model and good agreement was obtained, justifying the assumptions and simplifications made.
Because, by doing so, the local search technique forces the algorithm to search in the neighbouring area of the good solution to hope to find a better solution.
The evolutionary optimization not only gives the best parameter values but also, via the spread of the "good" solutions, allows to judge the importance of each parameter and of each experimental scenario used for the comparison.
