A comparison of distance metrics for the multi-objective pathfinding problem

Abstract

Abstract Pathfinding, also known as route planning, is one of the most important aspects of logistics, robotics, and other applications where engineers must balance many competing interests. There is a significant challenge in pathfinding problems with multiple objectives because many paths can map to the same objective value. Such multi-modal solutions cannot easily be found in multi-objective optimisation algorithms, which are typically geared towards selection mechanisms in the objective space. A niching approach for preserving good diverse solutions in the decision space is proposed in this paper, which is tailored for pathfinding problems. The criteria used to compare the solutions within the decision space are path similarity metrics, which we extend from a previous study, and are used instead of the well-established crowding distance. In two variations, we investigate the proposed meta-heuristic approach on a range of benchmark instances and compare the methodology to a deterministic optimisation approach.