Spatial genetic algorithm for multi-objective forest planning

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.