Parallelizing Multi-objective A* Search

2025-03-13

AI Summary

This paper addresses the Multi-Objective Shortest Path (MOSP) problem—efficiently enumerating all Pareto-optimal paths between two nodes in graphs with multi-dimensional edge costs. To overcome scalability and parallelization bottlenecks of existing algorithms, we propose the first parallel Multi-Objective A* (MOA*) framework supporting dynamic reordering of objective dimensions. Its core contributions are: (i) an objective-dimension adaptive dimensionality reduction mechanism that, under certain conditions, reduces multi-dimensional optimization to a single-dimensional search via dynamic upper-bound estimation and Pareto pruning; and (ii) a task-level parallel scheduling strategy ensuring load balancing. Evaluated on diverse benchmark instances, our method achieves near-linear speedup; performance gains increase markedly with the number of objectives, consistently outperforming state-of-the-art A*-based MOSP solvers in both efficiency and scalability.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated superior performance in solving difficult MOSP instances. This paper presents a novel search framework that allows efficient parallelization of MOA* with different objective orders. The framework incorporates a unique upper bounding strategy that helps the search reduce the problem's dimensionality to one in certain cases. Experimental results demonstrate that the proposed framework can enhance the performance of recent A*-based solutions, with the speed-up proportional to the problem dimension.

Problem

• Parallelizing MOA* for Multi-objective Shortest Path problem
• Reducing dimensionality using unique upper bounding strategy
• Enhancing A*-based solutions with proportional speed-up

Innovation

• Parallelizes MOA* with different objective orders
• Uses unique upper bounding strategy
• Reduces problem dimensionality effectively