Home > Mateda2.0 > ScriptsMateda > OptimizationScripts > VariantsGaussianEDAs_trajectory.m

# VariantsGaussianEDAs_trajectory

## PURPOSE

EXAMPLE 13: Different variants of continuous EDAs for a spacecraft

## SYNOPSIS

This is a script file.

## DESCRIPTION

     EXAMPLE 13:  Different variants of continuous EDAs for a spacecraft
     trajectory problem.
     For a description of the spacecraft trajectory problem and links
     to the problem instances see:
     Benchmarking different global optimisation techniques for preliminary
     space trajectory design (2007). Tamas Vinko and Dario Izzo and Claudio
     Bombardelli. Proceedings of 58th International Astronautical Congress

## CROSS-REFERENCE INFORMATION

This function calls:

- RunEDA [AllStat,Cache]=RunEDA(PopSize,n,F,Card,cache,edaparams)

This function is called by:

## SOURCE CODE

    0001  % EXAMPLE 13:  Different variants of continuous EDAs for a spacecraft
    0002  % trajectory problem.
    0003  % For a description of the spacecraft trajectory problem and links
    0004  % to the problem instances see:
    0005  % Benchmarking different global optimisation techniques for preliminary
    0006  % space trajectory design (2007). Tamas Vinko and Dario Izzo and Claudio
    0007  % Bombardelli. Proceedings of 58th International Astronautical Congress
    0008
    0009  global MGADSMproblem
    0010  cd ../trajectory
    0011  load EdEdJ  % This instance can be downloaded for ESA web page
    0012  cd ../Mateda2.0
    0013
    0014  NumbVar = 12;
    0015  PopSize = 1000;
    0016  F = 'EvalSaga'; % The function should be modified to enforce maximization, i.e g(x) = -1*f(x)
    0017  Card(1,:) = [7000,0,0,0,50,300,0.01,0.01,1.05,8,-1*pi,-1*pi];
    0018  Card(2,:) = [9100,7,1,1,2000,2000,0.90,0.90,7.00,500,pi,pi];
    0019  cache  = [0,0,0,0,0];
    0020  edaparams{1} = {'learning_method','LearnGaussianUnivModel',{}};
    0021  %edaparams{1} = {'learning_method','LearnGaussianFullModel',{}};
    0022  % edaparams{1} = {'learning_method','LearnGaussianNetwork',BN_params};
    0023  edaparams{2} = {'sampling_method','SampleGaussianUnivModel',{PopSize,1}};
    0024  % edaparams{2} = {'sampling_method','SampleGaussianFullModel',{PopSize,2}};
    0025  % BN_params(1:6) = {'k2',10,0.05,'pearson','bic','no'};
    0026  % edaparams{2} = {'sampling_method','SampleBN',{PopSize,1}};
    0027  edaparams{3} = {'replacement_method','best_elitism',{'fitness_ordering'}};
    0028  selparams(1:2) = {0.1,'fitness_ordering'};
    0029  edaparams{4} = {'selection_method','truncation_selection',selparams};
    0030  edaparams{5} = {'repairing_method','SetWithinBounds_repairing',{}};
    0031  edaparams{6} = {'stop_cond_method','max_gen',{5000}};
    0032  [AllStat,Cache]=RunEDA(PopSize,NumbVar,F,Card,cache,edaparams)