  The Multi-Objective Shortest Path Problem (MO-SPP), typically posed on a
graph, determines a set of paths from a start vertex to a destination vertex
while optimizing multiple objectives. In general, there does not exist a single
solution path that can simultaneously optimize all the objectives and the
problem thus seeks to find a set of so-called Pareto-optimal solutions. To
address this problem, several Multi-Objective A* (MOA*) algorithms were
recently developed to quickly compute solutions with quality guarantees.
However, these MOA* algorithms often suffer from high memory usage, especially
when the branching factor (i.e. the number of neighbors of any vertex) of the
graph is large. This work thus aims at reducing the high memory consumption of
MOA* with little increase in the runtime. By generalizing and unifying several
single- and multi-objective search algorithms, we develop the Runtime and
Memory Efficient MOA* (RME-MOA*) approach, which can balance between runtime
and memory efficiency by tuning two user-defined hyper-parameters.
