Towards an improvement of variable interaction identification for large-scale constrained problems

In this work, three modifications are proposed to improve the performance of the Variable Interaction Identification for Constrained problems (VIIC), a technique to detect interacting variables in large scale constrained numerical optimization problems. The changes proposed are: (1) the optimization of a single variable arrangement (the original VIIC needs to find an arrangement of variables for the objective function and also for each constraint), (2) two new strategies to generate a new arrangement, instead of the random generator of the original VIIC, and (3) simulated annealing as an optimizer instead of the greedy search adopted in the original VIIC. The results indicate the viability of using just one variable arrangement and the good performance provided by the two proposed strategies in the search, particularly combined with VIIC's original greedy search.