FOREST PLANNING USING PSO WITH A PRIORITY REPRESENTATION P.W. Brooks and W.D. Potter Institute for Artificial Intelligence, University of Georgia, USA Nature-Inspired Optimization

Overview  Background: (NIO Project 1 )  PSO -- GA -- EO -- RO  Diagnosis – Configuration -- Planning – Route Finding  Forest Planning (aka Harvest Scheduling)  73-Stand Daniel Pickett Forest  Particle Swarm Optimization  Priority Representation  Results 1 W.D. Potter, E. Drucker, P. Bettinger, F. Maier, D. Luper, M. Martin, M. Watkinson, G. Handy, and C. Hayes, “Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization”, in Natural Intelligence for Scheduling, Planning and Packing Problems, edited by Raymond Chiong, Springer-Verlag, Studies in Computational Intelligence (SCI), Nature-Inspired Optimization

Forest Planning Daniel Pickett Forest – 73 stands with access roads, ponds, and streams Nature-Inspired Optimization

Forest Planning  Even-flow harvest  Cutting occurs in one of three time periods  Each time period is 10 years in duration  A stand is only cut at most once  A plan may include un-cut stands  Adjacent cuts not allowed (same period)  Goal: achieve target harvest each period  Fitness: minimize plan error Nature-Inspired Optimization

Forest Planning Nature-Inspired Optimization

Particle Swarm Optimization (PSO)  Models behavior of large groups of animals such as flocks of birds  Individuals’ movement through search space is guided by Population momentum Individual velocity Best local and global individual Random influences  Continuous and discrete problem representations possible  A good general purpose algorithm Nature-Inspired Optimization

Particle Swarm Optimization (PSO) Nature-Inspired Optimization

PSO – Priority Representation Nature-Inspired Optimization

PSO – Priority Representation  Built-in constraint violation avoidance, but  Increased search space size (219 vs 73)  Real-valued priorities vs limited integer values  Longer processing time to generate a plan Nature-Inspired Optimization

PSO – Experiment Setup Nature-Inspired Optimization

Results (smaller error is better) NIO:GADPSOROEO Harvest6.5M35M5,500,39110M inertiapopsizePR best M M M M ,500, M Nature-Inspired Optimization

Conclusion  The priority representation is an effective way to encode harvest schedules for PSO  Ordering of plan elements by priority allows a PSO to deal with some constrained problems without requiring repairs or penalties  Minimal impact occurs to PSO structure  Minimal domain knowledge is required in order to apply the priority representation Nature-Inspired Optimization

Questions? Nature-Inspired Optimization

Thank You! Nature-Inspired Optimization

Genetic Algorithm (GA)  Models Evolution by Natural Selection Individuals (mates) are potential solutions Driving force is selection pressure (mate selection) Individuals mate to produce offspring (crossover) Mutation of offspring increases genetic variation Fitness function ranks individual fitness  Many variations are possible  Very powerful general purpose algorithm  Can be overly complicated to design Nature-Inspired Optimization

Extremal Optimization (EO)  Models tendency of systems to organize into non-equilibrium states Based on the Bak-Sneppen Model A single solution is evolved by changing the solution’s components Each component must also be assigned a fitness The worst component is randomly replaced  Useful for set covering and optimization problems  Component fitness may be difficult to calculate Nature-Inspired Optimization

Raindrop Method  Mimics the effect of falling rain A random position on the search landscape is chosen (rain drop) The chosen position’s value is randomly changed and all other positions are updated (water ripple) Updates may cause invalid states, so repair is necessary  Recently developed algorithm  Useful for certain map coloring problems Nature-Inspired Optimization