HCHAC: A family of MOACO algorithms for the resolution of the bi-criteria military unit pathfinding problem

Abstract

This paper presents a family of Multi-Objective Ant Colony Optimization (MOACO) algorithms, globally identified as hCHAC, which have been designed to solve a pathfinding problem in a military context considering two objectives: maximization of speed and safety. Each one of these objectives include different factors (such as stealth or avoidance of resource-consuming zones), that is why in this paper we generate different members of the hCHAC family by aggregating the initial cost functions into a different amount of objectives (from one to four) and considering a different parametrization set in each case. The hCHAC algorithms have been tested in several different (and increasingly realistic) scenarios, modelled in a simulator and compared with some other well-known MOACOs. These latter algorithms have been adapted for the purpose of this work to deal with this problem, along with a new multi-objective greedy approach that has been included as baseline for comparisons. The experiments show that most of the hCHAC algorithms outperform the other approaches, yielding at the same time very good military behaviour in the tactical sense. Within the hCHAC family, hCHAC-2, an approach considering two objectives, yields the best results overall.