[2067] | 1 | /* -*- C++ -*- |
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| 2 | * |
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| 3 | * This file is a part of LEMON, a generic C++ optimization library |
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| 4 | * |
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| 5 | * Copyright (C) 2003-2006 |
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| 6 | * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport |
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| 7 | * (Egervary Research Group on Combinatorial Optimization, EGRES). |
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| 8 | * |
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| 9 | * Permission to use, modify and distribute this software is granted |
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| 10 | * provided that this copyright notice appears in all copies. For |
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| 11 | * precise terms see the accompanying LICENSE file. |
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| 12 | * |
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| 13 | * This software is provided "AS IS" with no warranty of any kind, |
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| 14 | * express or implied, and with no claim as to its suitability for any |
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| 15 | * purpose. |
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| 16 | * |
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| 17 | */ |
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| 18 | |
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| 19 | |
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| 20 | #ifndef LEMON_TABU_SEARCH_H |
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| 21 | #define LEMON_TABU_SEARCH_H |
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| 22 | |
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| 23 | /// \ingroup experimental |
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| 24 | /// \file |
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| 25 | /// \brief TabuSearch algorithm. |
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| 26 | /// |
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| 27 | /// \author Szabadkai Mark |
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| 28 | |
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| 29 | #include <lemon/bits/utility.h> |
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| 30 | #include <lemon/error.h> |
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| 31 | #include <lemon/time_measure.h> |
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| 32 | #include <functional> |
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| 33 | #include <deque> |
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| 34 | |
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| 35 | |
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| 36 | namespace lemon { |
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| 37 | |
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| 38 | /// \brief Default Traits for TabuSearch class. |
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| 39 | /// |
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| 40 | /// This template defines the needed types for the \ref TabuSearch class. |
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| 41 | /// Is main purpos is to simplify the main class's template interface, |
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| 42 | /// but it provides the EdgeIt type, passing to the concrete graph wheter |
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| 43 | /// it is directed or undirected. |
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| 44 | #ifdef DOXYGEN |
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| 45 | template< typename GRAPH, typename VALUE, |
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| 46 | typename HEIGHTMAP, typename BETTER, bool UNDIR > |
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| 47 | #else |
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| 48 | template< typename GRAPH, typename VALUE, |
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| 49 | typename HEIGHTMAP = typename GRAPH::template NodeMap<VALUE>, |
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| 50 | typename BETTER = std::less<VALUE>, |
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| 51 | bool UNDIR = UndirectedTagIndicator<GRAPH>::value > |
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| 52 | #endif |
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| 53 | struct TabuSearchDefaultTraits { |
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| 54 | typedef VALUE Value; |
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| 55 | typedef BETTER Better; |
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| 56 | |
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| 57 | typedef GRAPH Graph; |
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| 58 | typedef typename GRAPH::Node Node; |
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| 59 | typedef HEIGHTMAP HeightMap; |
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| 60 | |
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| 61 | typedef typename GRAPH::IncEdgeIt EdgeIt; |
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| 62 | }; |
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| 63 | |
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| 64 | template< typename GRAPH, typename VALUE, |
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| 65 | typename HEIGHTMAP, typename BETTER > |
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| 66 | struct TabuSearchDefaultTraits< GRAPH, VALUE, HEIGHTMAP, BETTER, false > { |
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| 67 | typedef VALUE Value; |
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| 68 | typedef BETTER Better; |
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| 69 | |
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| 70 | typedef GRAPH Graph; |
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| 71 | typedef typename GRAPH::Node Node; |
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| 72 | typedef HEIGHTMAP HeightMap; |
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| 73 | |
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| 74 | typedef typename GRAPH::OutEdgeIt EdgeIt; |
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| 75 | }; |
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| 76 | |
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| 77 | |
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| 78 | |
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| 79 | /// \brief Policy hierarchy to controll the search algorithm. |
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| 80 | /// |
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| 81 | /// The fallowing template hierarchy offers a clean interface to define own |
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| 82 | /// policies, and combine existing ones. |
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| 83 | template< typename TS > |
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| 84 | struct TabuSearchPolicyConcept { |
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| 85 | void target( TS *ts ) {} |
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| 86 | |
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| 87 | void reset() {} |
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| 88 | bool onStep() { return false; } |
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| 89 | bool onStick() { return false; } |
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| 90 | bool onImprove( const typename TS::Value &best ) { return false; } |
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| 91 | }; |
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| 92 | |
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| 93 | template< typename TS > |
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| 94 | struct YesPolicy { |
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| 95 | void target( TS *ts ) {} |
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| 96 | |
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| 97 | void reset() {} |
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| 98 | bool onStep() { return true; } |
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| 99 | bool onStick() { return true; } |
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| 100 | bool onImprove( const typename TS::Value &best ) { return true; } |
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| 101 | }; |
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| 102 | |
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| 103 | template< typename TS > |
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| 104 | struct NoPolicy : public TabuSearchPolicyConcept<TS> {}; |
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| 105 | |
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| 106 | /// \brief Some basic methode, how tow Policies can be combined |
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| 107 | struct PolicyAndCombination { |
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| 108 | static bool evaluate( const bool r1, const bool r2 ) { |
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| 109 | return r1 && r2; |
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| 110 | } |
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| 111 | }; |
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| 112 | |
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| 113 | struct PolicyOrCombination { |
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| 114 | static bool evaluate( const bool r1, const bool r2 ) { |
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| 115 | return r1 || r2; |
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| 116 | } |
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| 117 | }; |
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| 118 | |
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| 119 | /// \brief CombinePolicies |
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| 120 | /// |
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| 121 | /// It combines tow policies using the given combination methode (mainly |
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| 122 | /// some of the basic logical methodes) to create a new one. |
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| 123 | #ifdef DOXYGEN |
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| 124 | template< template<typename> class CP1, template<typename> class CP2, |
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| 125 | typename COMBINATION > |
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| 126 | #else |
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| 127 | template< template<typename> class CP1, template<typename> class CP2, |
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| 128 | typename COMBINATION = PolicyAndCombination > |
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| 129 | #endif |
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| 130 | struct CombinePolicies { |
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| 131 | template< typename TS > |
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| 132 | struct Policy { |
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| 133 | typedef CP1<TS> Policy1; |
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| 134 | typedef CP2<TS> Policy2; |
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| 135 | |
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| 136 | Policy1 policy1; |
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| 137 | Policy2 policy2; |
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| 138 | |
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| 139 | inline Policy() : policy1(), policy2() {} |
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| 140 | inline Policy( const Policy1 &cp1, const Policy2 &cp2 ) |
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| 141 | : policy1(cp1), policy2(cp2) {} |
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| 142 | |
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| 143 | void target( TS *ts ) { |
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| 144 | policy1.target(ts), policy2.target(ts); |
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| 145 | }; |
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| 146 | |
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| 147 | void reset() { |
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| 148 | policy1.reset(), policy2.reset(); |
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| 149 | } |
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| 150 | |
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| 151 | bool onStep() { |
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| 152 | return cmb.evaluate( policy1.onStep(), policy2.onStep() ); |
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| 153 | } |
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| 154 | |
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| 155 | bool onStick() { |
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| 156 | return cmb.evaluate( policy1.onStick(), policy2.onStick() ); |
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| 157 | } |
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| 158 | |
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| 159 | bool onImprove( const typename TS::Value &best ) { |
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| 160 | return cmb.evaluate( policy1.onImprove(best), |
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| 161 | policy2.onImprove(best) ); |
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| 162 | } |
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| 163 | |
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| 164 | private: |
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| 165 | COMBINATION cmb; |
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| 166 | }; |
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| 167 | }; |
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| 168 | |
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| 169 | |
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| 170 | /// \brief IterationPolicy limits the number of iterations and the |
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| 171 | /// number of iterations without improvement |
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| 172 | template< typename TS > |
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| 173 | struct IterationPolicy { |
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| 174 | IterationPolicy() : _it_lim(100000), _noimpr_it_lim(5000) {} |
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| 175 | IterationPolicy( const long int itl, const long int noimpritl ) |
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| 176 | : _it_lim(itl), _noimpr_it_lim(noimpritl) |
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| 177 | {} |
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| 178 | |
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| 179 | void target( TS *ts ) {} |
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| 180 | |
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| 181 | void reset() { |
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| 182 | _it = _noimpr_it = 0; |
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| 183 | } |
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| 184 | |
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| 185 | bool onStep() { |
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| 186 | ++_it; ++_noimpr_it; |
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| 187 | return (_it <= _it_lim) && (_noimpr_it <= _noimpr_it_lim); |
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| 188 | } |
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| 189 | |
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| 190 | bool onStick() { |
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| 191 | return false; |
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| 192 | } |
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| 193 | |
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| 194 | bool onImprove( const typename TS::Value &best ) { |
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| 195 | _noimpr_it = 0; |
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| 196 | return true; |
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| 197 | } |
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| 198 | |
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| 199 | long int iterationLimit() const { |
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| 200 | return _it_lim; |
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| 201 | } |
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| 202 | |
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| 203 | void iterationLimit( const long int itl ) { |
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| 204 | _it_lim = itl; |
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| 205 | } |
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| 206 | |
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| 207 | long int noImprovementIterationLimit() const { |
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| 208 | return _noimpr_it_lim; |
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| 209 | } |
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| 210 | |
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| 211 | void noImprovementIterationLimit( const long int noimpritl ) { |
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| 212 | _noimpr_it_lim = noimpritl; |
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| 213 | } |
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| 214 | |
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| 215 | private: |
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| 216 | long int _it_lim, _noimpr_it_lim; |
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| 217 | long int _it, _noimpr_it; |
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| 218 | }; |
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| 219 | |
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| 220 | /// \brief HeightPolicy stops the search when a given height is reached or |
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| 221 | /// exceeds |
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| 222 | template< typename TS > |
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| 223 | struct HeightPolicy { |
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| 224 | typedef typename TS::Value Value; |
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| 225 | |
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| 226 | HeightPolicy() : _height_lim(), _found(false) {} |
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| 227 | HeightPolicy( const Value &hl ) : _height_lim(hl), _found(false) {} |
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| 228 | |
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| 229 | void target( TS *ts ) {} |
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| 230 | |
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| 231 | void reset() { |
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| 232 | _found = false; |
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| 233 | } |
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| 234 | |
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| 235 | bool onStep() { |
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| 236 | return !_found; |
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| 237 | } |
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| 238 | |
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| 239 | bool onStick() { |
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| 240 | return false; |
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| 241 | } |
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| 242 | |
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| 243 | bool onImprove( const Value &best ) { |
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| 244 | typename TS::Better better; |
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| 245 | _found = better(best, _height_lim) || (best == _height_lim); |
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| 246 | return !_found; |
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| 247 | } |
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| 248 | |
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| 249 | Value heightLimi() const { |
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| 250 | return _height_lim; |
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| 251 | } |
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| 252 | |
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| 253 | void heightLimi( const Value &hl ) { |
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| 254 | _height_lim = hl; |
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| 255 | } |
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| 256 | |
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| 257 | private: |
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| 258 | Value _height_lim; |
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| 259 | bool _found; |
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| 260 | }; |
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| 261 | |
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| 262 | /// \brief TimePolicy limits the time for searching. |
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| 263 | template< typename TS > |
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| 264 | struct TimePolicy { |
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| 265 | TimePolicy() : _time_lim(60.0), _timeisup(false) {} |
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| 266 | TimePolicy( const double tl ) : _time_lim(tl), _timeisup(false) {} |
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| 267 | |
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| 268 | void target( TS *ts ) {} |
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| 269 | |
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| 270 | void reset() { |
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| 271 | _timeisup = false; |
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| 272 | _t.reset(); |
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| 273 | } |
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| 274 | |
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| 275 | bool onStep() { |
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| 276 | update(); |
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| 277 | return !_timeisup; |
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| 278 | } |
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| 279 | |
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| 280 | bool onStick() { |
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| 281 | return false; |
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| 282 | } |
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| 283 | |
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| 284 | bool onImprove( const typename TS::Value &best ) { |
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| 285 | update(); |
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| 286 | return !_timeisup; |
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| 287 | } |
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| 288 | |
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| 289 | double timeLimit() const { |
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| 290 | return _time_lim; |
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| 291 | } |
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| 292 | |
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| 293 | void setTimeLimit( const double tl ) { |
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| 294 | _time_lim = tl; |
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| 295 | update(); |
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| 296 | } |
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| 297 | |
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| 298 | private: |
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| 299 | lemon::Timer _t; |
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| 300 | double _time_lim; |
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| 301 | bool _timeisup; |
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| 302 | |
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| 303 | inline void update() { |
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| 304 | _timeisup = _t.realTime() > _time_lim; |
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| 305 | } |
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| 306 | }; |
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| 307 | |
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| 308 | |
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| 309 | |
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| 310 | /// \brief TabuSearch main class |
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| 311 | /// |
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| 312 | /// This class offers the implementation of tabu-search algorithm. The |
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| 313 | /// tabu-serach is a local-search. It starts from a specified point of the |
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| 314 | /// problem's graph representation, and in every step it goes to the localy |
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| 315 | /// best next Node except those in tabu set. The maximum size of this tabu |
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| 316 | /// set defines how many Node will be remembered. The best Node ever found |
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| 317 | /// will also stored, so we wont lose it, even is the search continues. |
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| 318 | /// The class can be used on any kind of Graph and with any kind of Value |
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| 319 | /// with a total-settlement on it. |
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| 320 | /// |
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| 321 | /// \param _Graph The graph type the algorithm runs on. |
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| 322 | /// \param _Value The values' type associated to the nodes. |
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| 323 | /// \param _Policy Controlls the search. Determinates when to stop, or how |
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| 324 | /// manage stuck search. Default value is \ref IterationPolicy . |
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| 325 | /// \param _Traits Collection of needed types. Default value is |
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| 326 | /// \ref TabuSearchDefaultTraits . |
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| 327 | /// |
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| 328 | /// \author Szabadkai Mark |
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| 329 | #ifdef DOXYGEN |
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| 330 | template< typename GRAPH, typename VALUE, template<typename> class POLICY, typename TRAITS > |
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| 331 | #else |
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| 332 | template< typename GRAPH, typename VALUE, |
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| 333 | template<typename> class POLICY = IterationPolicy, |
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| 334 | typename TRAITS = TabuSearchDefaultTraits<GRAPH, VALUE> > |
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| 335 | #endif |
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| 336 | class TabuSearch |
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| 337 | { |
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| 338 | public: |
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| 339 | |
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| 340 | /// \brief Thrown by setting the size of the tabu-set and the given size |
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| 341 | /// is less than 2. |
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| 342 | class BadParameterError : public lemon::LogicError { |
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| 343 | public: |
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| 344 | virtual const char* exceptionName() const { |
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| 345 | return "lemon::TabuSearch::BadParameterError"; |
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| 346 | } |
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| 347 | }; |
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| 348 | |
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| 349 | ///Public types |
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| 350 | typedef TabuSearch<GRAPH,VALUE,POLICY,TRAITS> SelfType; |
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| 351 | |
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| 352 | typedef typename TRAITS::Graph Graph; |
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| 353 | typedef typename TRAITS::Node Node; |
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| 354 | typedef typename TRAITS::Value Value; |
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| 355 | typedef typename TRAITS::HeightMap HeightMap; |
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| 356 | typedef typename TRAITS::Better Better; |
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| 357 | typedef typename std::deque< Node >::const_iterator TabuIterator; |
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| 358 | |
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| 359 | typedef POLICY<SelfType> Policy; |
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| 360 | |
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| 361 | protected: |
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| 362 | typedef typename TRAITS::EdgeIt EdgeIt; |
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| 363 | |
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| 364 | const Graph &gr; |
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| 365 | const HeightMap &height; |
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| 366 | /// The tabu set. Teh current node is the first |
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| 367 | std::deque< Node > tabu; |
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| 368 | /// Maximal tabu size |
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| 369 | unsigned int mts; |
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| 370 | /// The best Node found |
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| 371 | Node b; |
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| 372 | |
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| 373 | Better better; |
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| 374 | Policy pol; |
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| 375 | |
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| 376 | public: |
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| 377 | /// \brief Constructor |
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| 378 | /// |
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| 379 | /// \param graph the graph the algorithm will run on. |
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| 380 | /// \param hm the height map used by the algorithm. |
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| 381 | /// \param tabusz the maximal size of the tabu set. Default value is 3 |
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| 382 | /// \param p the Policy controlling the search. |
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| 383 | TabuSearch( const Graph &graph, const HeightMap &hm, |
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| 384 | const int tabusz = 3, Policy p = Policy() ) |
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| 385 | : gr(graph), height(hm), mts(tabusz), pol(p) |
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| 386 | { |
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| 387 | pol.target(this); |
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| 388 | } |
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| 389 | |
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| 390 | /// \brief Destructor |
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| 391 | ~TabuSearch() { |
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| 392 | pol.target(NULL); |
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| 393 | } |
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| 394 | |
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| 395 | /// Set/Get the size of the tabu set |
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| 396 | void tabuSize( const unsigned int size ) |
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| 397 | { |
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| 398 | if( size < 2 ) |
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| 399 | throw BadParameterError( "Tabu size must be at least 2!" ); |
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| 400 | mts = size; |
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| 401 | while( mts < tabu.size() ) |
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| 402 | tabu.pop_back(); |
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| 403 | } |
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| 404 | |
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| 405 | unsigned int tabuSize() const { |
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| 406 | return mts; |
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| 407 | } |
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| 408 | |
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| 409 | /// Set/Get Policy |
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| 410 | void policy( Policy p ) { |
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| 411 | pol.target(NULL); |
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| 412 | pol = p; |
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| 413 | pol.target(this); |
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| 414 | } |
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| 415 | |
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| 416 | Policy& policy() { |
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| 417 | return pol; |
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| 418 | } |
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| 419 | |
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| 420 | /// \name Execution control |
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| 421 | /// The simplest way to execute the algorithm is to use the member |
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| 422 | /// functions called \c run( 'startnode' ). |
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| 423 | ///@{ |
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| 424 | |
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| 425 | /// \brief Initializes the internal data. |
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| 426 | /// |
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| 427 | /// \param startn The start node where the search begins. |
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| 428 | void init( const Node startn ) { |
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| 429 | tabu.clear(); |
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| 430 | tabu.push_front( startn ); |
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| 431 | b = startn; |
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| 432 | pol.reset(); |
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| 433 | } |
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| 434 | |
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| 435 | /// \brief Does one iteration |
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| 436 | /// |
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| 437 | /// If the Policy allows it searches for the best next node, then steps |
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| 438 | /// onto it. |
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| 439 | /// \return %False if one Policy condition wants to stop the search. |
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| 440 | bool step() |
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| 441 | { |
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| 442 | ///Request premmision from ControllPolicy |
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| 443 | if( !pol.onStep() ) |
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| 444 | return false; |
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| 445 | |
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| 446 | ///Find the best next potential node |
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| 447 | Node n; bool found = false; |
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| 448 | for( EdgeIt e(gr,tabu[0]); e != INVALID; ++e ) |
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| 449 | { |
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| 450 | Node m = (gr.source(e) == tabu[0]) ? gr.target(e) : gr.source(e); |
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| 451 | bool wrong = false; |
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| 452 | for( int i = 1; i != (signed int)tabu.size(); ++i ) |
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| 453 | if( m == tabu[i] ) { |
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| 454 | wrong = true; |
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| 455 | break; |
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| 456 | } |
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| 457 | if( wrong ) |
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| 458 | continue; |
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| 459 | |
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| 460 | if( !found ) { |
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| 461 | n = m; |
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| 462 | found = true; |
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| 463 | } else |
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| 464 | if( better(height[m], height[n]) ) { |
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| 465 | n = m; |
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| 466 | } |
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| 467 | } |
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| 468 | |
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| 469 | ///Handle stuck search |
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| 470 | if( !found ) { |
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| 471 | return pol.onStick(); |
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| 472 | } |
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| 473 | |
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| 474 | ///Move on... |
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| 475 | tabu.push_front(n); |
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| 476 | while( mts < tabu.size() ) { |
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| 477 | tabu.pop_back(); |
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| 478 | } |
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| 479 | if( better(height[n], height[b]) ) { |
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| 480 | b = n; |
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| 481 | if( !pol.onImprove(height[b]) ) |
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| 482 | return false; |
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| 483 | } |
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| 484 | |
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| 485 | return true; |
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| 486 | } |
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| 487 | |
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| 488 | /// \brief Runs a search while the Policy stops it. |
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| 489 | /// |
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| 490 | /// \param startn The start node where the search begins. |
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| 491 | inline void run( const Node startn ) { |
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| 492 | std::cin.unsetf( std::ios_base::skipws ); |
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| 493 | char c; |
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| 494 | init( startn ); |
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| 495 | while( step() ) |
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| 496 | std::cin >> c; |
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| 497 | std::cin.setf( std::ios_base::skipws ); |
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| 498 | } |
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| 499 | |
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| 500 | ///@} |
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| 501 | |
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| 502 | /// \name Query Functions |
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| 503 | /// The result of the TabuSearch algorithm can be obtained using these |
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| 504 | /// functions.\n |
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| 505 | ///@{ |
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| 506 | |
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| 507 | /// \brief The node, the search is standing on. |
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| 508 | inline Node current() const { |
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| 509 | return tabu[0]; |
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| 510 | } |
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| 511 | |
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| 512 | /// \brief The best node found until now. |
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| 513 | inline Node best() const { |
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| 514 | return b; |
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| 515 | } |
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| 516 | |
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| 517 | /// \brief Beginning to iterate on the current tabu set. |
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| 518 | inline TabuIterator tabu_begin() const { |
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| 519 | return tabu.begin(); |
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| 520 | } |
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| 521 | |
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| 522 | /// \brief Ending to iterate on the current tabu set. |
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| 523 | inline TabuIterator tabu_end() const { |
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| 524 | return tabu.end(); |
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| 525 | } |
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| 526 | |
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| 527 | ///@} |
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| 528 | }; |
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| 529 | } |
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| 530 | #endif |
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