TSP.cpp

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/*!
 * 
 *
 * \brief       A 10-city traveling salesman problem.
 
 * The implementation follows:
 * <p>
 * D. E. Goldberg and R. Lingle, Alleles, loci, and traveling
 * salesman problem. In <em> Proc. of the International Conference on
 * Genetic Algorithms and Their Applications</em>, pages 154-159,
 * Pittsburg, PA, 1985 </p> 
 
 * The traveling salsman problem is a combinatorial optimization task. A
 * salesman is supposed to visit $n$ cities. Each travelling connection
 * is associated with a cost (i.e. the time fot the trip). The problem is
 * to find the cheapest round-route that visits each city exactly once
 * and returns to the starting point.
 
 * 
 *
 * \author      T. Voss
 * \date        -
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
#include <shark/Algorithms/DirectSearch/Operators/Recombination/PartiallyMappedCrossover.h>
#include <shark/Algorithms/DirectSearch/Operators/Selection/RouletteWheelSelection.h>
#include <shark/Algorithms/DirectSearch/Individual.h>
#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
 
#include <shark/LinAlg/Base.h>
 
#include <boost/format.hpp>
#if BOOST_VERSION == 106000
#include <boost/type_traits/ice.hpp>//Required because of boost 1.60 bug
#endif
#include <boost/graph/adjacency_matrix.hpp>
#include <boost/graph/adjacency_list.hpp>
#include <boost/graph/graphml.hpp>
#include <boost/graph/graphviz.hpp>
#include <boost/graph/metric_tsp_approx.hpp>
#include <boost/property_map/dynamic_property_map.hpp>
#include <numeric>//for iota
 
using namespace shark;
typedef IntVector Tour;
 
/**
 * \brief Defines the problem, i.e., its cost matrix.
 */
const double cities[10][10] = {
    {       0,      28,     57,     72,     81,     85,     80,     113,    89,     80 },
    {       28,     0,      28,     45,     54,     57,     63,     85,     63,     63 },
    {       57,     28,     0,      20,     30,     28,     57,     57,     40,     57 },
    {       72,     45,     20,     0,      10,     20,     72,     45,     20,     45 },
    {       81,     54,     30,     10,     0,      22,     81,     41,     10,     41 },
    {       85,     57,     28,     20,     22,     0,      63,     28,     28,     63 },
    {       80,     63,     57,     72,     81,     63,     0,      80,     89,     113 },
    {       113,    85,     57,     45,     41,     28,     80,     0,      40,     80 },
    {       89,     63,     40,     20,     10,     28,     89,     40,     0,      40 },
    {       80,     63,     57,     45,     41,     63,     113,    80,     40,     0 }
};
 
typedef boost::adjacency_matrix< boost::undirectedS,
    boost::property< boost::vertex_color_t, std::string >,
    boost::property< boost::edge_weight_t, double,
        boost::property< boost::edge_color_t, std::string >
    >,
    boost::no_property 
> Graph;
typedef boost::graph_traits<Graph>::vertex_descriptor Vertex;
typedef boost::graph_traits<Graph>::edge_descriptor Edge;
typedef boost::property_map<Graph, boost::edge_weight_t>::type WeightMap;
typedef boost::property_map<Graph, boost::edge_color_t>::type ColorMap;
 
typedef Individual< Tour,double > IndividualType;
typedef std::vector< IndividualType > Population;
 
 
/// \brief Calculates the cost of a tour w.r.t. to a cost matrix.
struct TspTourLength : public SingleObjectiveFunction{
 
    /// \brief Default c'tor, initializes the cost matrix.
    TspTourLength( RealMatrix const& costMatrix) : m_costMatrix( costMatrix )
    { }
 
    std::string name() const
    { return "TspTourLength"; }
 
    std::size_t numberOfVariables() const
    { return m_costMatrix.size1(); }
 
    /**
     * \brief Calculates the costs of the supplied tour.
     */
    ResultType eval( const SearchPointType & input ) const {                    
        SIZE_CHECK( input.size() == m_costMatrix.size1() );
        m_evaluationCounter++;
        ResultType result(0);
        for( std::size_t i = 0; i < input.size() - 1; i++ ) {
            result += m_costMatrix( input( i ), input( i+1 ) );
        }
        return result;
    }
 
    RealMatrix m_costMatrix;
};
 
int main( int argc, char ** argv ) {
 
    // Define the problem instance
    Graph g( 10 );
    // Iterate the graph and assign a label to every vertex
    boost::graph_traits<Graph>::vertex_iterator v, v_end;
    for( boost::tie(v,v_end) = boost::vertices(g); v != v_end; ++v )
    boost::put(boost::vertex_color_t(), g, *v, ( boost::format( "City_%1%" ) % *v ).str() );
    // Get hold of the weight map and the color map for calculation and visualization purposes.
    WeightMap weightMap = boost::get( boost::edge_weight, g );
    ColorMap colorMap = boost::get( boost::edge_color, g );
    
    // Iterate the graph and insert distances.
    Edge e;
    bool inserted = false;
    
    RealMatrix costMatrix( 10, 10 );
    for( std::size_t i = 0; i < costMatrix.size1(); i++ ) {
        for( std::size_t j = 0; j < costMatrix.size1(); j++ ) {
 
            if( i == j ) continue;
 
            costMatrix(i,j) = cities[i][j];
 
            // Add the edge
            boost::tie( e, inserted ) = boost::add_edge( i, j, g );
            if( inserted ) {
                // Remember distance between cities i and j.
                weightMap[ e ] = cities[i][j];
                // Mark the edge as blue.
                colorMap[ e ] = "blue";
            }
        }
    }
 
    // Mating selection operator
    RouletteWheelSelection rws;
    // Variation (crossover) operator
    PartiallyMappedCrossover pmx;    
    // Fitness function instance.
    TspTourLength ttl( costMatrix );
    ttl.init();
 
    // Size of the parent population
    const std::size_t mu = 100;
    // Size of the offspring population
    const std::size_t lambda = 100;
 
    // Parent population
    Population parents( mu );
    // Offspring population
    Population offspring( lambda );
 
    // Default tour: 0,...,9
    Tour t( 10 );
    std::iota( t.begin(),t.end(), 0 );
 
    // Initialize parents
    for( std::size_t i = 0; i < parents.size(); i++ ) {
        parents[i].searchPoint() = t;
        // Generate a permutation.
        std::random_shuffle( parents[ i ].searchPoint().begin(), parents[ i ].searchPoint().end() );
        // Evaluate the individual.
        parents[i].penalizedFitness() = parents[i].unpenalizedFitness() = ttl( parents[i].searchPoint() );
    }
 
    // Loop until maximum number of fitness function evaluations is reached.
    while( ttl.evaluationCounter() < 10000 ) {
        RealVector selectionProbabilities(parents.size());
        for(std::size_t i = 0; i != parents.size(); ++i){
            selectionProbabilities(i) = parents[i].unpenalizedFitness();
        }
        selectionProbabilities/= sum(selectionProbabilities);
        // Crossover candidates.
        for( std::size_t i = 0; i < offspring.size() - 1; i+=2 ) {
            // Carry out fitness proportional fitness selection and
            // perform partially mapped crossover on parent individuals.
            offspring[ i ] = *rws( random::globalRng, parents.begin(), parents.end(), selectionProbabilities );
            offspring[ i+1 ] = *rws( random::globalRng, parents.begin(), parents.end(), selectionProbabilities );
            pmx(random::globalRng, offspring[ i ], offspring[ i+1 ]);
 
            // Evaluate offspring individuals.
            offspring[ i ].penalizedFitness() = 
            offspring[ i ].unpenalizedFitness() = ttl( offspring[ i ].searchPoint() );      
 
            offspring[ i+1 ].penalizedFitness() = 
            offspring[ i+1 ].unpenalizedFitness() = ttl( offspring[ i+1 ].searchPoint()  );
        }
 
        // Swap parent and offspring population.
        std::swap( parents, offspring );
    }
 
    // Sort in ascending order to find best individual.
    std::sort( parents.begin(), parents.end(), IndividualType::FitnessOrdering());
    
    // Extract the best tour currently known.
    Tour final = parents.front().searchPoint();
 
    // Mark the final tour green in the graph.
    bool extracted = false;
    for( std::size_t i = 0; i < final.size() - 1; i++ ) {
        boost::tie( e, extracted ) = boost::edge( Vertex( final( i ) ), Vertex( final( i + 1 ) ), g );
    
    if( extracted )
        colorMap[ e ] = "green";
    }
 
    // Calculate approximate solution
    double len = 0.0;
    std::vector< Vertex > approxTour;
    boost::metric_tsp_approx( g, boost::make_tsp_tour_len_visitor( g, std::back_inserter( approxTour ) , len, weightMap ) );
    // Mark the approximation red in the graph.
    for( std::size_t i = 0; i < approxTour.size() - 1; i++ ) {
    boost::tie( e, extracted ) = boost::edge( approxTour[ i ], approxTour[ i+1 ], g );
    
    if( extracted )
        colorMap[ e ] = "red";
    }
 
    // Output the graph and the final path
    std::ofstream outGraphviz( "graph.dot" );
    boost::dynamic_properties dp;
    dp.property( "node_id", boost::get( boost::vertex_color, g ) );
    dp.property( "weight", boost::get( boost::edge_weight, g ) );
    dp.property( "label", boost::get( boost::edge_weight, g ) );
    dp.property( "color", boost::get( boost::edge_color, g ) );
    boost::write_graphviz_dp( outGraphviz, g, dp );
 
    // Output best solution and corresponding fitness.
    std::cout << parents.front().searchPoint() << " -> " << parents.front().unpenalizedFitness() << std::endl;
    // Output approximate solution and corresponding fitness.
    std::cout << "Approx: " << len << " vs. GA: " << parents.front().unpenalizedFitness() << std::endl;
 
    return EXIT_SUCCESS;
}