A comparison of heuristic best-first algorithms for bicriterion shortest path problems

A variety of algorithms have been proposed to solve the bicriterion shortest path problem. This article analyzes and compares the performance of three best-first (label-setting) algorithms that accept heuristic information to improve efficiency. These are NAMOA∗, MOA∗, and Tung & Chew’s algorithm (TC). A set of experiments explores the impact of heuristic information in search efficiency, and the relative performance of the algorithms. The analysis reveals that NAMOA∗ is the best option for difficult problems. Its time performance can benefit considerably from heuristic information, though not in all cases. The performance of TC is similar but somewhat worse. However, the time performance of MOA∗ is found to degrade considerably with the use of heuristic information in most cases. Explanations are provided for these phenomena.

► Three heuristic multiobjective best-first (label setting) algorithms are compared.
► The influence of solution depth and correlation between objectives is considered.
► Heuristic algorithm NAMOA∗ is generally the best alternative.
► A class of problems is analysed where heuristics do not improve time performance.
► A very bad time performance of heuristic algorithm MOA∗ is observed and analysed.