There has been a lot of research to predict earthquakes and how to increase safety during an earthquake. A question that has remained relatively unexplored is what happens after an earthquake? And how can Artificial Intelligence help? The last question is a big issue to tackle, which we focused on in this challenge. Scientists predict that there will be an earthquake in Istanbul in the near future but the exact date is difficult to identify since Istanbul resides on a fault line.

In this paper we propose a Multi-Objective Ant Colony Optimization (MOACO) algorithm called CHAC, which has been designed to solve the problem of finding the path on a map (corresponding to a simulated battlefield) that minimizes resources while maximizing safety. CHAC has been tested with two different state transition rules: an aggregative function that combines the heuristic and pheromone information of both objectives and a second one that is based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in several different situations (maps with different degree of difficulty), and we have found that they yield better results than a greedy algorithm (taken as baseline) in addition to a military behaviour that is also better in the tactical sense. The aggregative function, in general, yields better results than the one based on dominance.