Algorithms for decision support in the battlefield have to take into account separately all factors with an impact of success: speed, visibility, and consumption of material and human resources. It is usual to combine several objectives, since military commanders give more importance to some factors than others, but it is interesting to also explore and optimize all objectives at the same time, to have a wider range of possible solutions to choose from, and explore more efficiently the space of all possible paths. In this paper we introduce hCHAC-4, the four-objective version of the hCHAC bi-objective ant colony optimization algorithm, and compare results obtained with them and also with some other approaches (extreme and mono-objective ones). It is concluded that this new version of the algorithm is more robust, and covers more efficiently the Pareto front of all possible solutions, so it can be consider as a better tool for military decision support.