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

ConsNet—A tabu search approach to the spatially coherent conservation area network design problem 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Heuristics Springer Journals

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

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References (41)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media, LLC
Subject
Mathematics; Operations Research, Management Science; Operation Research/Decision Theory; Artificial Intelligence (incl. Robotics); Calculus of Variations and Optimal Control; Optimization
ISSN
1381-1231
eISSN
1572-9397
DOI
10.1007/s10732-008-9098-7
Publisher site
See Article on Publisher Site

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.

Journal

Journal of HeuristicsSpringer Journals

Published: Sep 20, 2008

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