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portada New Techniques and Algorithms for Multiobjective and Lexicographic Goal-Based Shortest Path Problems
Type
Physical Book
Publisher
Author
Language
English
Pages
196
Format
Paperback
ISBN13
9783668132498

New Techniques and Algorithms for Multiobjective and Lexicographic Goal-Based Shortest Path Problems

Francisco J Pulido Arrebola (Author) · Grin Verlag · Paperback

New Techniques and Algorithms for Multiobjective and Lexicographic Goal-Based Shortest Path Problems - Francisco J Pulido Arrebola

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Synopsis "New Techniques and Algorithms for Multiobjective and Lexicographic Goal-Based Shortest Path Problems"

Doctoral Thesis / Dissertation from the year 2015 in the subject Computer Science - Miscellaneous, University of Málaga (University of Málaga), language: English, abstract: Shortest Path Problems (SPP) are one of the most extensively studied problems in the fields of Artificial Intelligence (AI) and Operations Research (OR). It consists in finding the shortest path between two given nodes in a graph such that the sum of the weights of its constituent arcs is minimized. However, real life problems frequently involve the consideration of multiple, and often conflicting, criteria. When multiple objectives must be simultaneously optimized, the concept of a single optimal solution is no longer valid. Instead, a set of efficient or Pareto-optimal solutions define the optimal trade-off between the objectives under consideration. The Multicriteria Search Problem (MSP), or Multiobjective Shortest Path Problem, is the natural extension to the SPP when more than one criterion are considered. The MSP is computationally harder than the single objective one. The number of label expansions can grow exponentially with solution depth, even for the two objective case. However, with the assumption of bounded integer costs and a fixed number of objectives the problem becomes tractable for polynomially sized graphs. Goal programming is one of the most successful Multicriteria Decision Making (MCDM) techniques used in Multicriteria Optimization. In this thesis we explore one of its variants in the MSP. Thus, we aim to solve the Multicriteria Search Problem with lexicographic goal-based preferences. To do so, we build on previous work on algorithm NAMOA∗, a successful extension of the A∗ algorithm to the multiobjective case. More precisely, we provide a new algorithm called LEXGO∗, an exact label-setting algorithm that returns the subset of Pareto optimal paths that satisfy a set of lexicographic goals, or the subset that minimizes deviation from goals if these cannot be fully satisfied

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