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Optimising Forest Management Using Multi-Objective Genetic Algorithms

Author

Listed:
  • Isabel Castro

    (Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal)

  • Raúl Salas-González

    (RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Coimbra Agriculture School, Polytechnic University of Coimbra, Bencanta, 3045-601 Coimbra, Portugal)

  • Beatriz Fidalgo

    (Coimbra Agriculture School, Polytechnic University of Coimbra, Bencanta, 3045-601 Coimbra, Portugal)

  • José Torres Farinha

    (Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal)

  • Mateus Mendes

    (Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal)

Abstract

Forest management requires balancing ecological, economic, and social objectives, often involving complex optimisation problems. Traditional mathematical methods struggle with these challenges, leading to the adoption of metaheuristic approaches like the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This paper introduces a custom NSGA-II algorithm, incorporating a specialised mutation operator to enhance solution generation for multi-objective forest planning. The custom NSGA-II is compared to the standard NSGA-II in a scenario aiming to maximise timber harvest volume and minimise its standard deviation, with a minimum volume constraint. Key performance metrics include non-dominated solutions, spacing, computational cost, and hypervolume. The results demonstrate that the custom NSGA-II provides more valid solutions and better explores the solution space. This approach offers a user-friendly and efficient tool for forest managers, integrating well with Web-based systems for modern, sustainability-oriented forest planning.

Suggested Citation

  • Isabel Castro & Raúl Salas-González & Beatriz Fidalgo & José Torres Farinha & Mateus Mendes, 2024. "Optimising Forest Management Using Multi-Objective Genetic Algorithms," Sustainability, MDPI, vol. 16(23), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10655-:d:1537230
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    References listed on IDEAS

    as
    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Ananda, Jayanath & Herath, Gamini, 2009. "A critical review of multi-criteria decision making methods with special reference to forest management and planning," Ecological Economics, Elsevier, vol. 68(10), pages 2535-2548, August.
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