Appendix A. Analysis of the time performance of heuristic NAMOA* and TC This appendix analyzes those cases where the time performance of heuristic NAMOA* and TC was found to be worse than that of blind NAMOA* (sections 4.2.1 and 6.2). This behavior was first reported in Machuca et al. (2009) for the case of ρ = 0 and class I problems. This work also pointed… (More)
This paper compares empirically the performance in time and space of two multiobjective graph search algorithms, MOA* and NAMOA*. Previous theoretical work has shown that NAMOA* is never worse than MOA*. Now, a statistical analysis is presented on the relative performance of both algorithms in space and time over sets of randomly generated problems.
This article considers the performance of the MOA* multiobjective search algorithm with heuristic information. It is shown that in certain cases blind search can be more efficient than perfectly informed search, in terms of both node and label expansions. A class of simple graph search problems is defined for which the number of nodes grows linearly with… (More)
This work evaluates two different approaches for multicriteria graph search problems using compromise preferences. This approach focuses search on a single solution that represents a balanced tradeoff between objectives, rather than on the whole set of Pareto optimal solutions. We review the main concepts underlying compromise preferences, and two main… (More)
This paper describes the application of multiobjective heuristic search algorithms to the problem of hazardous material (hazmat) transportation. The selection of optimal routes inherently involves the consideration of multiple conflicting objectives. These include the minimization of risk (e.g. the exposure of the population to hazardous substances in case… (More)
This thesis analyzes the performance of multiobjec-tive heuristic graph search algorithms. The analysis is focused on the influence of heuristic information, correlation between objectives and solution depth.