Optimising Forest Management Using Multi-Objective Genetic Algorithms

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 (N...

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Bibliographic Details
Published in:Sustainability Vol. 16; no. 23; p. 10655
Main Authors: Castro, Isabel, Salas-González, Raúl, Fidalgo, Beatriz, Farinha, José Torres, Mendes, Mateus
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2024
Subjects:
Adaptation
Climate change
Forest management
Forest products
Genetic algorithms
Harvest
Landowners
Learning
Mathematical optimization
Methods
Multiple criteria decision making
Objectives
Pareto optimum
Planning
Sustainability
Timber
Wood
Portugal
ISSN:2071-1050, 2071-1050
Online Access:Get full text
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Summary: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.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su162310655