Towards sustainable forest management strategies with MOEAs

Sustainable forest management is a crucial element in combating climate change, plastic pollution, and other unsolved challenges of the 21st century. Forests not only produce wood - a renewable resource that is increasingly replacing fossil-based materials - but also preserve biodiversity and store massive amounts of carbon. Thus, a truly optimal forest policy has to balance profit-oriented logging with ecological and societal interests, and should thus be solved as a multi-objective optimization problem. Economic forest research, however, has largely focused on profit maximization. Recent publications still scalarize the problem a priori by assigning weights to objectives. In this paper, we formulate a multi-objective forest management problem where profit, carbon storage, and biodiversity are maximized. We obtain Pareto-efficient forest management strategies by utilizing three state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs), and by incorporating domain-specific knowledge through customized evolutionary operators. An analysis of Pareto-efficient strategies and their harvesting schedules in the design space clearly shows the benefits of the proposed approach. Unlike many EMO application studies, we demonstrate how a systematic post-optimality trade-off analysis can be applied to choose a single preferred solution. Our pioneering work on sustainable forest management explores an entirely new application area for MOEAs with great societal impact.

[1]  Timo Pukkala,et al.  Species Interactions in the Dynamics of Even- and Uneven-Aged Boreal Forests , 2013 .

[2]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[3]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[4]  Daisuke Sasaki,et al.  Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map , 2003, EMO.

[5]  Kalyanmoy Deb,et al.  Pymoo: Multi-Objective Optimization in Python , 2020, IEEE Access.

[6]  Zhuo Cheng,et al.  Metsäammattilaisten suhtautuminen metsän erirakenteiskasvatukseen , 2014 .

[7]  Janne Rämö,et al.  Economics of mixed-species forestry with ecosystem services , 2019, Canadian Journal of Forest Research.

[8]  Kalyanmoy Deb,et al.  An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm , 2010, Evolutionary Computation.

[9]  K. Miettinen,et al.  Interactive bundle-based method for nondifferentiable multiobjeective optimization: nimbus § , 1995 .

[10]  Timo Saksa,et al.  Structure and yield of all-sized and even-sized Scots pine-dominated stands , 1994 .

[11]  E. Viitala,et al.  Timber, science and statecraft: the emergence of modern forest resource economic thought in Germany , 2016, European Journal of Forest Research.

[12]  O. Tahvonen,et al.  Economics of size-structured forestry with carbon storage , 2018 .

[13]  Joshua D. Knowles,et al.  Bounded archiving using the lebesgue measure , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[14]  Erkki Lähde,et al.  Silvicultural alternatives in an uneven-sized forest dominated by Picea abies , 2010, Journal of Forest Research.

[15]  E. Lähde,et al.  Growth and diversity effects of silvicultural alternatives on an old‐growth forest in Finland , 2002 .

[16]  Terje Gobakken,et al.  T: A forest simulator for bioeconomic analyses based on models for individual trees , 2008 .

[17]  M. Faustmann,et al.  Calculation of the value which forest land and immature stands possess for forestry , 1995 .

[18]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[19]  Kalyanmoy Deb,et al.  A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications , 2017, IEEE Transactions on Evolutionary Computation.

[20]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[21]  Kerstin Vogler,et al.  Applications Of Multi Objective Evolutionary Algorithms , 2016 .

[22]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[23]  Lily Rachmawati,et al.  Multiobjective Evolutionary Algorithm With Controllable Focus on the Knees of the Pareto Front , 2009, IEEE Transactions on Evolutionary Computation.

[24]  Janne Rämö,et al.  Optimality of continuous cover vs. clear-cut regimes in managing forest resources , 2016 .

[25]  Aino Assmuth,et al.  Economics of boreal conifer species in continuous cover and rotation forestry , 2019, Forest Policy and Economics.

[26]  Aravind Srinivasan,et al.  Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization , 2008, Multiobjective Problem Solving from Nature.

[27]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[28]  Yacov Y. Haimes,et al.  The Interactive Surrogate Worth Trade-Off (ISWT) Method for Multiobjective Decision-Making , 1978 .

[29]  Jorge Nocedal,et al.  Knitro: An Integrated Package for Nonlinear Optimization , 2006 .

[30]  Pekka Korhonen,et al.  A pareto race , 1988 .

[31]  W. Bossert,et al.  The Measurement of Diversity , 2001 .

[32]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[33]  Gary B. Lamont,et al.  AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS , 2004 .

[34]  H. Peltola,et al.  Effects of even-aged and uneven-aged management on carbon dynamics and timber yield in boreal Norway spruce stands: a forest ecosystem model approach , 2019, Forestry: An International Journal of Forest Research.

[35]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[36]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[37]  Patrice Marcotte,et al.  An overview of bilevel optimization , 2007, Ann. Oper. Res..

[38]  Per Angelstam,et al.  Uneven-aged forest management in boreal Sweden: local forestry stakeholders’ perceptions of different sustainability dimensions , 2011 .