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Spatial genetic algorithm for multi-objective forest planning
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DOI：10.1016/j.forpol.2012.04.002.png)
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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. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Spatial genetic algorithm
Multi-objective optimization
Spatial forest planning
Timber harvest allocation
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3.8
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被引数:
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