CEC Theses and Dissertations

Date of Award

2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science (CISD)

Department

College of Engineering and Computing

Advisor

Michael J. Lazlo

Committee Member

Sumitra Mukherjee

Committee Member

Francisco J. Mitropoulos

Abstract

The shortest path problem in graphs is both a classic combinatorial optimization problem and a practical problem that admits many applications. Techniques for preprocessing a graph are useful for reducing shortest path query times. This dissertation studies the foundations of a class of algorithms that use preprocessed landmark information and the triangle inequality to guide A* search in graphs. A new heuristic is presented for solving shortest path queries that enables the use of higher order polygon inequalities. We demonstrate this capability by leveraging distance information from two landmarks when visiting a vertex as opposed to the common single landmark paradigm. The new heuristic’s novel feature is that it computes and stores a reduced amount of preprocessed information (in comparison to previous landmark-based algorithms) while enabling more informed search decisions. We demonstrate that domination of this heuristic over its predecessor depends on landmark selection and that, in general, the denser the landmark set, the better heuristic performs. Due to the reduced memory requirement, this new heuristic admits much denser landmark sets.

We conduct experiments to characterize the impact that landmark configurations have on this new heuristic, demonstrating that centrality-based landmark selection has the best tradeoff between preprocessing and runtime. Using a developed graph library and static information from benchmark road map datasets, the algorithm is compared experimentally with previous landmark-based shortest path techniques in a fixed-memory environment to demonstrate a reduction in overall computational time and memory requirements. Experimental results are evaluated to detail the significance of landmark selection and density, the tradeoffs of performing preprocessing, and the practical use cases of the algorithm.

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