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Heuristic Optimisation

  • Annika Kangas
  • Mikko Kurttila
  • Teppo Hujala
  • Kyle Eyvindson
  • Jyrki Kangas
Chapter
  • 910 Downloads
Part of the Managing Forest Ecosystems book series (MAFE, volume 30)

Abstract

Many forest management planning problems include characteristics that make them hard to be solved by the use of linear programming-based methods. For example, management planning problems that involve spatial aspects commonly require the use of heuristics. The number of heuristic algorithms designed for solving such problems has been rapidly increasing during the last decade. In this chapter, we present some groups of methods, such as methods based on local improvements, population-based methods and reduced-cost-based methods that originate from linear programming. In addition, decentralised methods such as cellular automaton are introduced. We describe some of the algorithms belonging to each of these groups in more detail, for instance, genetic algorithms, simulated annealing and tabu search. Additionally, we describe for what sort of planning cases the different methods might be suitable and what are the benefits and drawbacks of those methods in forest planning.

Keywords

Metaheuristics Local and global optima Initial solution Neighbourhood Iteration Stopping rule Population-based methods Centralised and decentralised algorithms 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Annika Kangas
    • 1
  • Mikko Kurttila
    • 2
  • Teppo Hujala
    • 3
  • Kyle Eyvindson
    • 4
  • Jyrki Kangas
    • 5
  1. 1.Economics and SocietyNatural Resources Institute Finland (Luke)JoensuuFinland
  2. 2.Bio-based Business and IndustryNatural Resources Institute Finland (Luke)JoensuuFinland
  3. 3.Bio-based Business and IndustryNatural Resources Institute Finland (Luke)HelsinkiFinland
  4. 4.Department of Forest SciencesUniversity of HelsinkiHelsinkiFinland
  5. 5.School of Forest SciencesUniversity of Eastern FinlandJoensuuFinland

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