Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning.

We describe here an example of applying particle swarm optimization (PSO) — a population-based heuristic technique — to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.

[1]  Xuhui Chen,et al.  A Hybrid Algorithm Based on PSO and Simulated Annealing and Its Applications for Partner Selection in Virtual Enterprise , 2005, ICIC.

[2]  William F. Watson,et al.  Costs and cost trends for forestry practices in the South , 1987 .

[3]  Guangzhao Cui,et al.  Modified PSO algorithm for solving planar graph coloring problem , 2008 .

[4]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[5]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Lee,et al.  [American Institute of Aeronautics and Astronautics 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Austin, Texas ()] 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Aeroelastic Studies on a Folding Wing Configuration , 2005 .

[8]  W. D. Potter,et al.  Forest Planning Using Particle Swarm Optimization with a Priority Representation , 2011, IEA/AIE.

[9]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[10]  Ning Zhong,et al.  A Hybrid Discrete Particle Swarm Optimization for the Traveling Salesman Problem , 2006, SEAL.

[11]  John Sessions,et al.  Using Tabu search to schedule timber harvests subject to spatial wildlife goals for big game , 1997 .

[12]  John Sessions,et al.  A review of the status and use of validation procedures for heuristics used in forest planning , 2009, Math. Comput. For. Nat. Resour. Sci..

[13]  Jiao Li,et al.  Improved Particle Swarm Optimization algorithm based on statistical laws and dynamic learning factors , 2008 .

[14]  I N Kugelmass.  THE CRITERIA IN THE DECLARATION OF CHEMICAL INDEPENDENCE IN THE UNITED STATES. , 1918, Science.

[15]  J. Borges,et al.  Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models , 2002 .

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[17]  Ling Wang,et al.  No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm , 2008 .

[18]  Russell C. Eberhart,et al.  Computational intelligence - concepts to implementations , 2007 .

[19]  Pete Bettinger,et al.  Spatial forest plan development with ecological and economic goals , 2003 .

[20]  Mahamed G. H. Omran.  Particle swarm optimization methods for pattern recognition and image processing , 2006 .

[21]  José G. Borges,et al.  Designing an evolution program for solving integer forest management scheduling models : An application in Portugal , 2001 .

[22]  John Sessions,et al.  Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems , 2002 .

[23]  Jianping Zhu,et al.  A New Heuristic Method for Solving Spatially Constrained Forest Planning Problems Based on Mitigation of Infeasibilities Radiating Outward from a Forced Choice , 2006 .

[24]  Eric Drucker,et al.  Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization , 2009, Natural Intelligence for Scheduling, Planning and Packing Problems.

[25]  Jianping Zhu,et al.  Landscape-level optimization using tabu search and stand density-related forest management prescriptions , 2007, Eur. J. Oper. Res..

[26]  Miguel Constantino,et al.  A New Mixed-Integer Programming Model for Harvest Scheduling Subject to Maximum Area Restrictions , 2008, Oper. Res..

[27]  Kevin Boston,et al.  Combining tabu search and genetic algorithm heuristic techniques to solve spatial harvest scheduling problems , 2002 .

[28]  Jianping Zhu,et al.  Additional insight into the performance of a new heuristic for solving spatially constrained forest planning problems , 2007 .

[29]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[30]  Eldon A. Gunn,et al.  Tabu search design for difficult forest management optimization problems , 2003 .

[31]  Maurice Clerc,et al.  MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm , 2011, Comput. Optim. Appl..

[32]  José G. Borges,et al.  Integrating fire risk in stand management scheduling. An application to Maritime pine stands in Portugal , 2014, Ann. Oper. Res..

[33]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[34]  Alcherio Martinoli,et al.  Discrete Multi-Valued Particle Swarm Optimization , 2006 .

[35]  Marc E. McDill,et al.  Comparing adjacency constraint formulations for randomly generated forest planning problems with four age-class distributions. , 2000 .

[36]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[37]  G. A. Jordan,et al.  Forest landscape management modeling using simulated annealing , 2002 .

[38]  T. Pukkala.  Population-based methods in the optimization of stand management. , 2009 .

[39]  Richard F. Daniels,et al.  Spatially explicit sustainability analysis of long-term fiber supply in Georgia, USA , 2004 .

[40]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[41]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[42]  Pete Bettinger,et al.  The Key Literature of, and Trends in, Forest-Level Management Planning in North America, 1950–2001 , 2004 .

[43]  Alan T. Murray.  Spatial restrictions in harvest scheduling , 1999 .