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Spatial genetic algorithm for multi-objective forest planning

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  • Fotakis, Dimitris G.
  • Sidiropoulos, Epameinondas
  • Myronidis, Dimitriοs
  • Ioannou, Kostas

Abstract

A spatial operator for genetic algorithms is introduced herein, in order to improve their efficiency for multi-objective spatial forest planning. Constrained NSGA-II is used as a standard genetic algorithm for the integration and the evaluation of the proposed methodology. A typical harvest scheduling problem is examined with two objectives: a) maximization of timber volume and b) minimization of sediment levels. Two constraints are imposed: a) minimum timber yield and b) even‐flows. The proposed algorithm (Spatial NSGA), gives better results for both the constrained and the unconstrained problem. Moreover, it achieves old forest compactness, although it is neither a separate objective nor a constraint, but it renders compactness as an emergent result. The purpose of the suggested approach is to support forestry decision-making by generating a set of optimal management alternatives. The implementation of the method produced a Pareto front consisting of non-dominating solutions and showing the tradeoffs between timber harvest and sediment levels in water runoff. The present approach offers potential applications to a wide spectrum of spatial planning problems beyond the one examined in this paper.

Suggested Citation

  • Fotakis, Dimitris G. & Sidiropoulos, Epameinondas & Myronidis, Dimitriοs & Ioannou, Kostas, 2012. "Spatial genetic algorithm for multi-objective forest planning," Forest Policy and Economics, Elsevier, vol. 21(C), pages 12-19.
    • Handle: RePEc:eee:forpol:v:21:y:2012:i:c:p:12-19
    DOI: 10.1016/j.forpol.2012.04.002
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    Cited by:

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    2. Liu, Wan-Yu & Lin, Chun-Cheng & Su, Ke-Hong, 2017. "Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions," Forest Policy and Economics, Elsevier, vol. 78(C), pages 51-66.
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    4. Dimitrios Myronidis & Charalambos Papageorgiou & Stavros Theophanous, 2016. "Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 245-263, March.
    5. Augustynczik, Andrey Lessa Derci & Yousefpour, Rasoul & Rodriguez, Luiz Carlos Estraviz & Hanewinkel, Marc, 2018. "Conservation Costs of Retention Forestry and Optimal Habitat Network Selection in Southwestern Germany," Ecological Economics, Elsevier, vol. 148(C), pages 92-102.
    6. Yan Mao & Yanfang Liu & Haofeng Wang & Wei Tang & Xuesong Kong, 2017. "A Spatial-Territorial Reorganization Model of Rural Settlements Based on Graph Theory and Genetic Optimization," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    7. Xiaoya Ma & Xiang Zhao, 2015. "Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China," Sustainability, MDPI, vol. 7(11), pages 1-20, November.
    8. Correa, Renata Naoko & Scarpin, Cassius Tadeu & Ferrari, Linamara Smaniotto & Arce, Julio Eduardo, 2020. "Application of relax-and-fix heuristic in the aggregation of stands for tactical forest scheduling," Forest Policy and Economics, Elsevier, vol. 119(C).

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