69 Appendix E GENETIK Genetic algorithm for the sizing cases research Hélène Pasquier Stéphane Clouet (CNES, France)
70 GENETIK Genetic algorithm for the sizing cases research Abstract GENETIK is a CNES internal tool which use genetic algorithm for sizing cases research. A first development phase has been started in 2005, with a validation on simple cases. In 2009, a second development and validation phase has led to an optimized tool in term of results and methodology. The objectives of the presentation are the following: Describe the principles of genetic algorithms, Focus on optimization in GENETIK in term of algorithm operators and methodology, Present validation cases and results.
GENETIK Genetic algorithm for the sizing cases research 71 GENETIK : Genetic algorithms for the sizing cases research PASQUIER Hélène, Thermal Engineer, CNES CLOUET Stéphane, CNES trainee 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 2 / 24
72 GENETIK Genetic algorithm for the sizing cases research Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 3 / 24 Context of the study New project complex orbit and many possible attitude (random attitude for example) More complicated to determine the dimensioning case for thermal analysis Use of genetic algorithms for sizing cases research training period in 2005 has led to a first internal tool GENETIK 2009 : new training period to Improve knowledge on genetic algorithms Optimize GENETIK Validate the internal tool on simple cases 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 4 / 24
GENETIK Genetic algorithm for the sizing cases research 73 Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 5 / 24 Genetic Algorithms - Presentation Search technique used to find solutions to optimization problems Technique inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Vocabulary : Gene : parameter of the problem (ex. : altitude of the satellite) Individual : combination of genes Population : set of individual Fitness : evaluation function to optimize (most of the time temperature) 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 6 / 24
74 GENETIK Genetic algorithm for the sizing cases research Genetic Algorithms - Presentation General architecture of the algorithm : First population PARENTS Generation iteration Mutation Crossing over CONDOR CHILDRENS Evaluation Selection Convergence criteria? NO YES GAETAN SYSTEMA End of simulation 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 7 / 24 Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 8 / 24
GENETIK Genetic algorithm for the sizing cases research 75 Genetic Algorithms - Operator optimization Mutation 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 9 / 24 Genetic Algorithms - Operator optimization Crossover One point crossover 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 10 / 24
76 GENETIK Genetic algorithm for the sizing cases research Genetic Algorithms - Operator optimization Crossover Barycentric crossover 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 11 / 24 Genetic Algorithms - Operator optimization SELECTION : Keep the best individuals to converge but keep diversity to avoid convergence to a local optimum Two different ways : - elitism - roulette wheel Selection WHAT IS A INTERESTING INDIVIDUAL FOR SELECTION : An individual with a good fitness An individual which is far from the other good individuals An individual which dominate a zone in the search space Elitism threshold on fitness Threshold on each gene of the individual 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 12 / 24
GENETIK Genetic algorithm for the sizing cases research 77 Genetic Algorithms - Operator optimization Selection 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 13 / 24 Genetic Algorithms - Operator optimization Selection Elitist selection 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 14 / 24
78 GENETIK Genetic algorithm for the sizing cases research Genetic Algorithms - Operator optimization Selection Roulette wheel selection 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 15 / 24 Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 16 / 24
GENETIK Genetic algorithm for the sizing cases research 79 Example On a simple geometry (cube with anisotropic conductivity) Sun synchronous orbit Tested parameters : Altitude : [ 700 ; 900 ] km Solar hour at ascending node : [ 06h00 ; 12h30 ] Albedo : [ 0.35 ; 0.50 ] Attitude vector 1 : { z, -z } Attitude vector 2 : {x, -x, y, -y} Day of the year : [ 1 ; 365 ] 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 17 / 24 Example Protocol First simulation : IDENTIFICATION of the important gene optimization of the selection parameter Second simulation CONFIRMATION of all the local optima Third and last simulation LOCAL EXPLORATION around the local optima to determine the global one 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 18 / 24
80 GENETIK Genetic algorithm for the sizing cases research Example first simulation Red points : fitness close to the best fitness ( T < 4 C) Blue points : fitness far from the best fitness ( T > 4 C) Day X X X X X Altitude X X X X Albedo X X X Solar hour X X Vector 1 X altitude albedo Solar hour Vector 1 Vector 2 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 19 / 24 Example first simulation Altitude / solar hour at ascending node correlation Orbit altitude (km) solar hour at ascending node (h) 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 20 / 24
GENETIK Genetic algorithm for the sizing cases research 81 Example second simulation Altitude / solar hour at ascending node correlation Orbit altitude (km) solar hour at ascending node (h) 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 21 / 24 Example third simulation Local simulation on the two optima : New local optima Final sizing case : Solar hour = 06h00 Day = 242 Fitness = -62,51 C 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 22 / 24
82 GENETIK Genetic algorithm for the sizing cases research Agenda Context of the study Genetic Algorithms Presentation Operator optimization Example Conclusion 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 23 / 24 Conclusion GENETIK is today optimized Validation on a simple case definition of a user protocol Simulation duration ~50 to 70h on one processor New development to allow a continuous exploration for attitude After a complete validation, integration in GAETAN V5 23rd European Workshop on Thermal and ECLS Software, 6-7 October 2009, ESA/ESTEC 24 / 24