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Anytime Algorithms for Biobjective Heuristic Search

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AI 2012: Advances in Artificial Intelligence (AI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

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Abstract

Heuristic search algorithms (MOA*, NAMOA* etc) for multiobjective optimization problems, when run with admissible heuristics, typically spend a lot of time and require huge amount of memory for generating the Pareto optimal solution frontier due to the fact that these algorithms expand all nodes/paths whose cost is not dominated by any other optimal solution. In this paper, we present an anytime heuristic search framework for biobjective optimization problems named “Anytime Biobjective Search (ABS)” which quickly finds a set of nondominated solutions and keeps on improving the solution frontier with time. The proposed framework uses the upper and lower limit estimates on one of the objectives to split the search space into a given number of segments and independently runs a particular search algorithm (branch-and-bound, beam search etc.) within each of the segments. In this paper, we present how existing search strategies, branch-and-bound, beam, and beam-stack, can be used within the proposed framework. Experimental results reveal that our proposed framework achieves good anytime performance.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, West Bengal, India, 721302

    Priyankar Ghosh, Partha Pratim Chakrabarti & Pallab Dasgupta

Authors
  1. Priyankar Ghosh
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  2. Partha Pratim Chakrabarti
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  3. Pallab Dasgupta
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Ghosh, P., Chakrabarti, P.P., Dasgupta, P. (2012). Anytime Algorithms for Biobjective Heuristic Search. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_20

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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