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ConsNet—A tabu search approach to the spatially coherent conservation area network design problem

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Abstract

This paper presents a new approach to the solution of the well-studied conservation area network design problem (CANP), which is closely related to the classical set cover problem (SCP). The goal is to find the smallest amount of land that (when placed under conservation) will contain and protect a specified representation level of biodiversity resources. A new tabu search methodology is applied to an extension of the “basic” CANP which explicitly considers additional spatial requirements for improved conservation planning. The underlying search engine, modular adaptive self-learning tabu search (MASTS), incorporates state-of-the-art techniques including adaptive tabu search, dynamic neighborhood selection, and rule-based objectives. The ability to utilize intransitive orderings within a rule-based objective gives the search flexibility, improving solution quality while saving computation. This paper demonstrates how rule-based objectives can be used to design near optimal conservation area networks in which the individual conservation areas are well connected. The results represent a considerable improvement over classical techniques that do not consider spatial features. This paper provides an initial description of ConsNet, a comprehensive software package for systematic conservation planning.

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Authors and Affiliations

  1. Graduate Program in Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, USA

    Michael Ciarleglio & J. Wesley Barnes

  2. Section of Integrative Biology, The University of Texas at Austin, Austin, USA

    Sahotra Sarkar

Authors
  1. Michael Ciarleglio
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  2. J. Wesley Barnes
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  3. Sahotra Sarkar
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Correspondence to J. Wesley Barnes.

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Ciarleglio, M., Barnes, J.W. & Sarkar, S. ConsNet—A tabu search approach to the spatially coherent conservation area network design problem. J Heuristics 16, 537–557 (2010). https://doi.org/10.1007/s10732-008-9098-7

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