Enhanced Multi-Objective A* with Partial Expansion

TL;DR

This paper introduces RME-MOA*, an enhanced multi-objective A* algorithm that reduces memory usage in solving MO-SPP by tuning hyper-parameters, maintaining efficiency and solution quality.

Contribution

It presents a unified, flexible approach that balances runtime and memory consumption in multi-objective A* search algorithms.

Findings

Significantly reduces memory usage compared to existing MOA* algorithms.

Maintains comparable runtime and solution quality with tunable hyper-parameters.

Unifies various single- and multi-objective search algorithms into a single framework.

Abstract

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.