[999] | 1 | /* -*- mode: C++; indent-tabs-mode: nil; -*- |
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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) 2003-2010 |
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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 | #ifndef LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
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| 20 | #define LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
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| 21 | |
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| 22 | /// \ingroup approx_algs |
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| 23 | /// |
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| 24 | /// \file |
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| 25 | /// \brief The iterated local search algorithm of Grosso, Locatelli, and Pullan |
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| 26 | /// for the maximum clique problem |
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| 27 | |
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| 28 | #include <vector> |
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| 29 | #include <limits> |
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| 30 | #include <lemon/core.h> |
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| 31 | #include <lemon/random.h> |
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| 32 | |
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| 33 | namespace lemon { |
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| 34 | |
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| 35 | /// \addtogroup approx_algs |
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| 36 | /// @{ |
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| 37 | |
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| 38 | /// \brief Implementation of the iterated local search algorithm of Grosso, |
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| 39 | /// Locatelli, and Pullan for the maximum clique problem |
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| 40 | /// |
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| 41 | /// \ref GrossoLocatelliPullanMc implements the iterated local search |
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| 42 | /// algorithm of Grosso, Locatelli, and Pullan for solving the \e maximum |
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| 43 | /// \e clique \e problem \ref grosso08maxclique. |
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| 44 | /// It is to find the largest complete subgraph (\e clique) in an |
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| 45 | /// undirected graph, i.e., the largest set of nodes where each |
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| 46 | /// pair of nodes is connected. |
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| 47 | /// |
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| 48 | /// This class provides a simple but highly efficient and robust heuristic |
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[1022] | 49 | /// method that quickly finds a quite large clique, but not necessarily the |
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[999] | 50 | /// largest one. |
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[1022] | 51 | /// The algorithm performs a certain number of iterations to find several |
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| 52 | /// cliques and selects the largest one among them. Various limits can be |
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| 53 | /// specified to control the running time and the effectiveness of the |
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| 54 | /// search process. |
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[999] | 55 | /// |
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| 56 | /// \tparam GR The undirected graph type the algorithm runs on. |
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| 57 | /// |
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| 58 | /// \note %GrossoLocatelliPullanMc provides three different node selection |
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| 59 | /// rules, from which the most powerful one is used by default. |
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| 60 | /// For more information, see \ref SelectionRule. |
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| 61 | template <typename GR> |
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| 62 | class GrossoLocatelliPullanMc |
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| 63 | { |
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| 64 | public: |
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| 65 | |
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| 66 | /// \brief Constants for specifying the node selection rule. |
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| 67 | /// |
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| 68 | /// Enum type containing constants for specifying the node selection rule |
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| 69 | /// for the \ref run() function. |
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| 70 | /// |
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| 71 | /// During the algorithm, nodes are selected for addition to the current |
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| 72 | /// clique according to the applied rule. |
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| 73 | /// In general, the PENALTY_BASED rule turned out to be the most powerful |
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| 74 | /// and the most robust, thus it is the default option. |
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| 75 | /// However, another selection rule can be specified using the \ref run() |
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| 76 | /// function with the proper parameter. |
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| 77 | enum SelectionRule { |
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| 78 | |
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| 79 | /// A node is selected randomly without any evaluation at each step. |
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| 80 | RANDOM, |
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| 81 | |
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| 82 | /// A node of maximum degree is selected randomly at each step. |
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| 83 | DEGREE_BASED, |
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| 84 | |
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| 85 | /// A node of minimum penalty is selected randomly at each step. |
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| 86 | /// The node penalties are updated adaptively after each stage of the |
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| 87 | /// search process. |
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| 88 | PENALTY_BASED |
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| 89 | }; |
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| 90 | |
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[1022] | 91 | /// \brief Constants for the causes of search termination. |
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| 92 | /// |
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| 93 | /// Enum type containing constants for the different causes of search |
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| 94 | /// termination. The \ref run() function returns one of these values. |
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| 95 | enum TerminationCause { |
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| 96 | |
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| 97 | /// The iteration count limit is reached. |
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| 98 | ITERATION_LIMIT, |
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| 99 | |
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| 100 | /// The step count limit is reached. |
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| 101 | STEP_LIMIT, |
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| 102 | |
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| 103 | /// The clique size limit is reached. |
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| 104 | SIZE_LIMIT |
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| 105 | }; |
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| 106 | |
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[999] | 107 | private: |
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| 108 | |
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| 109 | TEMPLATE_GRAPH_TYPEDEFS(GR); |
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| 110 | |
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| 111 | typedef std::vector<int> IntVector; |
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| 112 | typedef std::vector<char> BoolVector; |
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| 113 | typedef std::vector<BoolVector> BoolMatrix; |
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| 114 | // Note: vector<char> is used instead of vector<bool> for efficiency reasons |
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| 115 | |
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[1022] | 116 | // The underlying graph |
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[999] | 117 | const GR &_graph; |
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| 118 | IntNodeMap _id; |
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| 119 | |
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| 120 | // Internal matrix representation of the graph |
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| 121 | BoolMatrix _gr; |
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| 122 | int _n; |
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[1022] | 123 | |
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| 124 | // Search options |
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| 125 | bool _delta_based_restart; |
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| 126 | int _restart_delta_limit; |
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| 127 | |
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| 128 | // Search limits |
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| 129 | int _iteration_limit; |
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| 130 | int _step_limit; |
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| 131 | int _size_limit; |
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[999] | 132 | |
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| 133 | // The current clique |
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| 134 | BoolVector _clique; |
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| 135 | int _size; |
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| 136 | |
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| 137 | // The best clique found so far |
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| 138 | BoolVector _best_clique; |
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| 139 | int _best_size; |
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| 140 | |
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| 141 | // The "distances" of the nodes from the current clique. |
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| 142 | // _delta[u] is the number of nodes in the clique that are |
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| 143 | // not connected with u. |
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| 144 | IntVector _delta; |
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| 145 | |
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| 146 | // The current tabu set |
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| 147 | BoolVector _tabu; |
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| 148 | |
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| 149 | // Random number generator |
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| 150 | Random _rnd; |
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| 151 | |
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| 152 | private: |
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| 153 | |
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| 154 | // Implementation of the RANDOM node selection rule. |
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| 155 | class RandomSelectionRule |
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| 156 | { |
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| 157 | private: |
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| 158 | |
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| 159 | // References to the algorithm instance |
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| 160 | const BoolVector &_clique; |
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| 161 | const IntVector &_delta; |
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| 162 | const BoolVector &_tabu; |
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| 163 | Random &_rnd; |
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| 164 | |
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| 165 | // Pivot rule data |
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| 166 | int _n; |
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| 167 | |
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| 168 | public: |
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| 169 | |
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| 170 | // Constructor |
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| 171 | RandomSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 172 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 173 | _rnd(mc._rnd), _n(mc._n) |
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| 174 | {} |
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| 175 | |
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| 176 | // Return a node index for a feasible add move or -1 if no one exists |
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| 177 | int nextFeasibleAddNode() const { |
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| 178 | int start_node = _rnd[_n]; |
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| 179 | for (int i = start_node; i != _n; i++) { |
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| 180 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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| 181 | } |
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| 182 | for (int i = 0; i != start_node; i++) { |
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| 183 | if (_delta[i] == 0 && !_tabu[i]) return i; |
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| 184 | } |
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| 185 | return -1; |
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| 186 | } |
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| 187 | |
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| 188 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 189 | int nextFeasibleSwapNode() const { |
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| 190 | int start_node = _rnd[_n]; |
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| 191 | for (int i = start_node; i != _n; i++) { |
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| 192 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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| 193 | } |
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| 194 | for (int i = 0; i != start_node; i++) { |
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| 195 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i]) return i; |
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| 196 | } |
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| 197 | return -1; |
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| 198 | } |
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| 199 | |
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| 200 | // Return a node index for an add move or -1 if no one exists |
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| 201 | int nextAddNode() const { |
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| 202 | int start_node = _rnd[_n]; |
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| 203 | for (int i = start_node; i != _n; i++) { |
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| 204 | if (_delta[i] == 0) return i; |
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| 205 | } |
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| 206 | for (int i = 0; i != start_node; i++) { |
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| 207 | if (_delta[i] == 0) return i; |
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| 208 | } |
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| 209 | return -1; |
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| 210 | } |
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| 211 | |
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| 212 | // Update internal data structures between stages (if necessary) |
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| 213 | void update() {} |
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| 214 | |
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| 215 | }; //class RandomSelectionRule |
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| 216 | |
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| 217 | |
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| 218 | // Implementation of the DEGREE_BASED node selection rule. |
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| 219 | class DegreeBasedSelectionRule |
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| 220 | { |
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| 221 | private: |
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| 222 | |
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| 223 | // References to the algorithm instance |
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| 224 | const BoolVector &_clique; |
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| 225 | const IntVector &_delta; |
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| 226 | const BoolVector &_tabu; |
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| 227 | Random &_rnd; |
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| 228 | |
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| 229 | // Pivot rule data |
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| 230 | int _n; |
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| 231 | IntVector _deg; |
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| 232 | |
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| 233 | public: |
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| 234 | |
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| 235 | // Constructor |
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| 236 | DegreeBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 237 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 238 | _rnd(mc._rnd), _n(mc._n), _deg(_n) |
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| 239 | { |
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| 240 | for (int i = 0; i != _n; i++) { |
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| 241 | int d = 0; |
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| 242 | BoolVector &row = mc._gr[i]; |
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| 243 | for (int j = 0; j != _n; j++) { |
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| 244 | if (row[j]) d++; |
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| 245 | } |
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| 246 | _deg[i] = d; |
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| 247 | } |
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| 248 | } |
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| 249 | |
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| 250 | // Return a node index for a feasible add move or -1 if no one exists |
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| 251 | int nextFeasibleAddNode() const { |
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| 252 | int start_node = _rnd[_n]; |
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| 253 | int node = -1, max_deg = -1; |
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| 254 | for (int i = start_node; i != _n; i++) { |
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| 255 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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| 256 | node = i; |
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| 257 | max_deg = _deg[i]; |
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| 258 | } |
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| 259 | } |
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| 260 | for (int i = 0; i != start_node; i++) { |
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| 261 | if (_delta[i] == 0 && !_tabu[i] && _deg[i] > max_deg) { |
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| 262 | node = i; |
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| 263 | max_deg = _deg[i]; |
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| 264 | } |
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| 265 | } |
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| 266 | return node; |
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| 267 | } |
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| 268 | |
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| 269 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 270 | int nextFeasibleSwapNode() const { |
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| 271 | int start_node = _rnd[_n]; |
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| 272 | int node = -1, max_deg = -1; |
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| 273 | for (int i = start_node; i != _n; i++) { |
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| 274 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 275 | _deg[i] > max_deg) { |
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| 276 | node = i; |
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| 277 | max_deg = _deg[i]; |
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| 278 | } |
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| 279 | } |
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| 280 | for (int i = 0; i != start_node; i++) { |
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| 281 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 282 | _deg[i] > max_deg) { |
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| 283 | node = i; |
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| 284 | max_deg = _deg[i]; |
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| 285 | } |
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| 286 | } |
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| 287 | return node; |
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| 288 | } |
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| 289 | |
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| 290 | // Return a node index for an add move or -1 if no one exists |
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| 291 | int nextAddNode() const { |
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| 292 | int start_node = _rnd[_n]; |
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| 293 | int node = -1, max_deg = -1; |
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| 294 | for (int i = start_node; i != _n; i++) { |
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| 295 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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| 296 | node = i; |
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| 297 | max_deg = _deg[i]; |
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| 298 | } |
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| 299 | } |
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| 300 | for (int i = 0; i != start_node; i++) { |
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| 301 | if (_delta[i] == 0 && _deg[i] > max_deg) { |
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| 302 | node = i; |
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| 303 | max_deg = _deg[i]; |
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| 304 | } |
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| 305 | } |
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| 306 | return node; |
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| 307 | } |
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| 308 | |
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| 309 | // Update internal data structures between stages (if necessary) |
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| 310 | void update() {} |
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| 311 | |
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| 312 | }; //class DegreeBasedSelectionRule |
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| 313 | |
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| 314 | |
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| 315 | // Implementation of the PENALTY_BASED node selection rule. |
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| 316 | class PenaltyBasedSelectionRule |
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| 317 | { |
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| 318 | private: |
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| 319 | |
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| 320 | // References to the algorithm instance |
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| 321 | const BoolVector &_clique; |
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| 322 | const IntVector &_delta; |
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| 323 | const BoolVector &_tabu; |
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| 324 | Random &_rnd; |
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| 325 | |
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| 326 | // Pivot rule data |
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| 327 | int _n; |
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| 328 | IntVector _penalty; |
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| 329 | |
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| 330 | public: |
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| 331 | |
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| 332 | // Constructor |
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| 333 | PenaltyBasedSelectionRule(GrossoLocatelliPullanMc &mc) : |
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| 334 | _clique(mc._clique), _delta(mc._delta), _tabu(mc._tabu), |
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| 335 | _rnd(mc._rnd), _n(mc._n), _penalty(_n, 0) |
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| 336 | {} |
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| 337 | |
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| 338 | // Return a node index for a feasible add move or -1 if no one exists |
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| 339 | int nextFeasibleAddNode() const { |
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| 340 | int start_node = _rnd[_n]; |
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| 341 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 342 | for (int i = start_node; i != _n; i++) { |
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| 343 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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| 344 | node = i; |
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| 345 | min_p = _penalty[i]; |
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| 346 | } |
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| 347 | } |
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| 348 | for (int i = 0; i != start_node; i++) { |
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| 349 | if (_delta[i] == 0 && !_tabu[i] && _penalty[i] < min_p) { |
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| 350 | node = i; |
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| 351 | min_p = _penalty[i]; |
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| 352 | } |
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| 353 | } |
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| 354 | return node; |
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| 355 | } |
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| 356 | |
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| 357 | // Return a node index for a feasible swap move or -1 if no one exists |
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| 358 | int nextFeasibleSwapNode() const { |
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| 359 | int start_node = _rnd[_n]; |
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| 360 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 361 | for (int i = start_node; i != _n; i++) { |
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| 362 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 363 | _penalty[i] < min_p) { |
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| 364 | node = i; |
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| 365 | min_p = _penalty[i]; |
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| 366 | } |
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| 367 | } |
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| 368 | for (int i = 0; i != start_node; i++) { |
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| 369 | if (!_clique[i] && _delta[i] == 1 && !_tabu[i] && |
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| 370 | _penalty[i] < min_p) { |
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| 371 | node = i; |
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| 372 | min_p = _penalty[i]; |
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| 373 | } |
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| 374 | } |
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| 375 | return node; |
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| 376 | } |
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| 377 | |
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| 378 | // Return a node index for an add move or -1 if no one exists |
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| 379 | int nextAddNode() const { |
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| 380 | int start_node = _rnd[_n]; |
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| 381 | int node = -1, min_p = std::numeric_limits<int>::max(); |
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| 382 | for (int i = start_node; i != _n; i++) { |
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| 383 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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| 384 | node = i; |
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| 385 | min_p = _penalty[i]; |
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| 386 | } |
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| 387 | } |
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| 388 | for (int i = 0; i != start_node; i++) { |
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| 389 | if (_delta[i] == 0 && _penalty[i] < min_p) { |
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| 390 | node = i; |
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| 391 | min_p = _penalty[i]; |
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| 392 | } |
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| 393 | } |
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| 394 | return node; |
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| 395 | } |
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| 396 | |
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| 397 | // Update internal data structures between stages (if necessary) |
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| 398 | void update() {} |
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| 399 | |
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| 400 | }; //class PenaltyBasedSelectionRule |
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| 401 | |
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| 402 | public: |
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| 403 | |
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| 404 | /// \brief Constructor. |
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| 405 | /// |
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| 406 | /// Constructor. |
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| 407 | /// The global \ref rnd "random number generator instance" is used |
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| 408 | /// during the algorithm. |
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| 409 | /// |
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| 410 | /// \param graph The undirected graph the algorithm runs on. |
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| 411 | GrossoLocatelliPullanMc(const GR& graph) : |
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| 412 | _graph(graph), _id(_graph), _rnd(rnd) |
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[1022] | 413 | { |
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| 414 | initOptions(); |
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| 415 | } |
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[999] | 416 | |
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| 417 | /// \brief Constructor with random seed. |
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| 418 | /// |
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| 419 | /// Constructor with random seed. |
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| 420 | /// |
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| 421 | /// \param graph The undirected graph the algorithm runs on. |
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| 422 | /// \param seed Seed value for the internal random number generator |
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| 423 | /// that is used during the algorithm. |
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| 424 | GrossoLocatelliPullanMc(const GR& graph, int seed) : |
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| 425 | _graph(graph), _id(_graph), _rnd(seed) |
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[1022] | 426 | { |
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| 427 | initOptions(); |
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| 428 | } |
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[999] | 429 | |
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| 430 | /// \brief Constructor with random number generator. |
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| 431 | /// |
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| 432 | /// Constructor with random number generator. |
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| 433 | /// |
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| 434 | /// \param graph The undirected graph the algorithm runs on. |
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| 435 | /// \param random A random number generator that is used during the |
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| 436 | /// algorithm. |
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| 437 | GrossoLocatelliPullanMc(const GR& graph, const Random& random) : |
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| 438 | _graph(graph), _id(_graph), _rnd(random) |
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[1022] | 439 | { |
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| 440 | initOptions(); |
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| 441 | } |
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[999] | 442 | |
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| 443 | /// \name Execution Control |
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[1022] | 444 | /// The \ref run() function can be used to execute the algorithm.\n |
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| 445 | /// The functions \ref iterationLimit(int), \ref stepLimit(int), and |
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| 446 | /// \ref sizeLimit(int) can be used to specify various limits for the |
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| 447 | /// search process. |
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| 448 | |
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[999] | 449 | /// @{ |
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[1022] | 450 | |
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| 451 | /// \brief Sets the maximum number of iterations. |
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| 452 | /// |
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| 453 | /// This function sets the maximum number of iterations. |
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| 454 | /// Each iteration of the algorithm finds a maximal clique (but not |
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| 455 | /// necessarily the largest one) by performing several search steps |
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| 456 | /// (node selections). |
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| 457 | /// |
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| 458 | /// This limit controls the running time and the success of the |
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| 459 | /// algorithm. For larger values, the algorithm runs slower, but it more |
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| 460 | /// likely finds larger cliques. For smaller values, the algorithm is |
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| 461 | /// faster but probably gives worse results. |
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| 462 | /// |
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| 463 | /// The default value is \c 1000. |
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| 464 | /// \c -1 means that number of iterations is not limited. |
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| 465 | /// |
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| 466 | /// \warning You should specify a reasonable limit for the number of |
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| 467 | /// iterations and/or the number of search steps. |
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| 468 | /// |
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| 469 | /// \return <tt>(*this)</tt> |
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| 470 | /// |
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| 471 | /// \sa stepLimit(int) |
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| 472 | /// \sa sizeLimit(int) |
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| 473 | GrossoLocatelliPullanMc& iterationLimit(int limit) { |
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| 474 | _iteration_limit = limit; |
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| 475 | return *this; |
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| 476 | } |
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| 477 | |
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| 478 | /// \brief Sets the maximum number of search steps. |
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| 479 | /// |
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| 480 | /// This function sets the maximum number of elementary search steps. |
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| 481 | /// Each iteration of the algorithm finds a maximal clique (but not |
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| 482 | /// necessarily the largest one) by performing several search steps |
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| 483 | /// (node selections). |
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| 484 | /// |
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| 485 | /// This limit controls the running time and the success of the |
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| 486 | /// algorithm. For larger values, the algorithm runs slower, but it more |
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| 487 | /// likely finds larger cliques. For smaller values, the algorithm is |
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| 488 | /// faster but probably gives worse results. |
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| 489 | /// |
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| 490 | /// The default value is \c -1, which means that number of steps |
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| 491 | /// is not limited explicitly. However, the number of iterations is |
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| 492 | /// limited and each iteration performs a finite number of search steps. |
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| 493 | /// |
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| 494 | /// \warning You should specify a reasonable limit for the number of |
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| 495 | /// iterations and/or the number of search steps. |
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| 496 | /// |
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| 497 | /// \return <tt>(*this)</tt> |
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| 498 | /// |
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| 499 | /// \sa iterationLimit(int) |
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| 500 | /// \sa sizeLimit(int) |
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| 501 | GrossoLocatelliPullanMc& stepLimit(int limit) { |
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| 502 | _step_limit = limit; |
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| 503 | return *this; |
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| 504 | } |
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| 505 | |
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| 506 | /// \brief Sets the desired clique size. |
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| 507 | /// |
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| 508 | /// This function sets the desired clique size that serves as a search |
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| 509 | /// limit. If a clique of this size (or a larger one) is found, then the |
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| 510 | /// algorithm terminates. |
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| 511 | /// |
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| 512 | /// This function is especially useful if you know an exact upper bound |
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| 513 | /// for the size of the cliques in the graph or if any clique above |
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| 514 | /// a certain size limit is sufficient for your application. |
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| 515 | /// |
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| 516 | /// The default value is \c -1, which means that the size limit is set to |
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| 517 | /// the number of nodes in the graph. |
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| 518 | /// |
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| 519 | /// \return <tt>(*this)</tt> |
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| 520 | /// |
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| 521 | /// \sa iterationLimit(int) |
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| 522 | /// \sa stepLimit(int) |
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| 523 | GrossoLocatelliPullanMc& sizeLimit(int limit) { |
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| 524 | _size_limit = limit; |
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| 525 | return *this; |
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| 526 | } |
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| 527 | |
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| 528 | /// \brief The maximum number of iterations. |
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| 529 | /// |
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| 530 | /// This function gives back the maximum number of iterations. |
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| 531 | /// \c -1 means that no limit is specified. |
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| 532 | /// |
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| 533 | /// \sa iterationLimit(int) |
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| 534 | int iterationLimit() const { |
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| 535 | return _iteration_limit; |
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| 536 | } |
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| 537 | |
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| 538 | /// \brief The maximum number of search steps. |
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| 539 | /// |
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| 540 | /// This function gives back the maximum number of search steps. |
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| 541 | /// \c -1 means that no limit is specified. |
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| 542 | /// |
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| 543 | /// \sa stepLimit(int) |
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| 544 | int stepLimit() const { |
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| 545 | return _step_limit; |
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| 546 | } |
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| 547 | |
---|
| 548 | /// \brief The desired clique size. |
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| 549 | /// |
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| 550 | /// This function gives back the desired clique size that serves as a |
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| 551 | /// search limit. \c -1 means that this limit is set to the number of |
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| 552 | /// nodes in the graph. |
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| 553 | /// |
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| 554 | /// \sa sizeLimit(int) |
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| 555 | int sizeLimit() const { |
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| 556 | return _size_limit; |
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| 557 | } |
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[999] | 558 | |
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| 559 | /// \brief Runs the algorithm. |
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| 560 | /// |
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[1022] | 561 | /// This function runs the algorithm. If one of the specified limits |
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| 562 | /// is reached, the search process terminates. |
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[999] | 563 | /// |
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| 564 | /// \param rule The node selection rule. For more information, see |
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| 565 | /// \ref SelectionRule. |
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| 566 | /// |
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[1022] | 567 | /// \return The termination cause of the search. For more information, |
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| 568 | /// see \ref TerminationCause. |
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| 569 | TerminationCause run(SelectionRule rule = PENALTY_BASED) |
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[999] | 570 | { |
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| 571 | init(); |
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| 572 | switch (rule) { |
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| 573 | case RANDOM: |
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[1022] | 574 | return start<RandomSelectionRule>(); |
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[999] | 575 | case DEGREE_BASED: |
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[1022] | 576 | return start<DegreeBasedSelectionRule>(); |
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| 577 | default: |
---|
| 578 | return start<PenaltyBasedSelectionRule>(); |
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[999] | 579 | } |
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| 580 | } |
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| 581 | |
---|
| 582 | /// @} |
---|
| 583 | |
---|
| 584 | /// \name Query Functions |
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[1022] | 585 | /// The results of the algorithm can be obtained using these functions.\n |
---|
| 586 | /// The run() function must be called before using them. |
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| 587 | |
---|
[999] | 588 | /// @{ |
---|
| 589 | |
---|
| 590 | /// \brief The size of the found clique |
---|
| 591 | /// |
---|
| 592 | /// This function returns the size of the found clique. |
---|
| 593 | /// |
---|
| 594 | /// \pre run() must be called before using this function. |
---|
| 595 | int cliqueSize() const { |
---|
| 596 | return _best_size; |
---|
| 597 | } |
---|
| 598 | |
---|
| 599 | /// \brief Gives back the found clique in a \c bool node map |
---|
| 600 | /// |
---|
| 601 | /// This function gives back the characteristic vector of the found |
---|
| 602 | /// clique in the given node map. |
---|
| 603 | /// It must be a \ref concepts::WriteMap "writable" node map with |
---|
| 604 | /// \c bool (or convertible) value type. |
---|
| 605 | /// |
---|
| 606 | /// \pre run() must be called before using this function. |
---|
| 607 | template <typename CliqueMap> |
---|
| 608 | void cliqueMap(CliqueMap &map) const { |
---|
| 609 | for (NodeIt n(_graph); n != INVALID; ++n) { |
---|
| 610 | map[n] = static_cast<bool>(_best_clique[_id[n]]); |
---|
| 611 | } |
---|
| 612 | } |
---|
| 613 | |
---|
| 614 | /// \brief Iterator to list the nodes of the found clique |
---|
| 615 | /// |
---|
| 616 | /// This iterator class lists the nodes of the found clique. |
---|
| 617 | /// Before using it, you must allocate a GrossoLocatelliPullanMc instance |
---|
| 618 | /// and call its \ref GrossoLocatelliPullanMc::run() "run()" method. |
---|
| 619 | /// |
---|
| 620 | /// The following example prints out the IDs of the nodes in the found |
---|
| 621 | /// clique. |
---|
| 622 | /// \code |
---|
| 623 | /// GrossoLocatelliPullanMc<Graph> mc(g); |
---|
| 624 | /// mc.run(); |
---|
| 625 | /// for (GrossoLocatelliPullanMc<Graph>::CliqueNodeIt n(mc); |
---|
| 626 | /// n != INVALID; ++n) |
---|
| 627 | /// { |
---|
| 628 | /// std::cout << g.id(n) << std::endl; |
---|
| 629 | /// } |
---|
| 630 | /// \endcode |
---|
| 631 | class CliqueNodeIt |
---|
| 632 | { |
---|
| 633 | private: |
---|
| 634 | NodeIt _it; |
---|
| 635 | BoolNodeMap _map; |
---|
| 636 | |
---|
| 637 | public: |
---|
| 638 | |
---|
| 639 | /// Constructor |
---|
| 640 | |
---|
| 641 | /// Constructor. |
---|
| 642 | /// \param mc The algorithm instance. |
---|
| 643 | CliqueNodeIt(const GrossoLocatelliPullanMc &mc) |
---|
| 644 | : _map(mc._graph) |
---|
| 645 | { |
---|
| 646 | mc.cliqueMap(_map); |
---|
| 647 | for (_it = NodeIt(mc._graph); _it != INVALID && !_map[_it]; ++_it) ; |
---|
| 648 | } |
---|
| 649 | |
---|
| 650 | /// Conversion to \c Node |
---|
| 651 | operator Node() const { return _it; } |
---|
| 652 | |
---|
| 653 | bool operator==(Invalid) const { return _it == INVALID; } |
---|
| 654 | bool operator!=(Invalid) const { return _it != INVALID; } |
---|
| 655 | |
---|
| 656 | /// Next node |
---|
| 657 | CliqueNodeIt &operator++() { |
---|
| 658 | for (++_it; _it != INVALID && !_map[_it]; ++_it) ; |
---|
| 659 | return *this; |
---|
| 660 | } |
---|
| 661 | |
---|
| 662 | /// Postfix incrementation |
---|
| 663 | |
---|
| 664 | /// Postfix incrementation. |
---|
| 665 | /// |
---|
| 666 | /// \warning This incrementation returns a \c Node, not a |
---|
| 667 | /// \c CliqueNodeIt as one may expect. |
---|
| 668 | typename GR::Node operator++(int) { |
---|
| 669 | Node n=*this; |
---|
| 670 | ++(*this); |
---|
| 671 | return n; |
---|
| 672 | } |
---|
| 673 | |
---|
| 674 | }; |
---|
| 675 | |
---|
| 676 | /// @} |
---|
| 677 | |
---|
| 678 | private: |
---|
[1022] | 679 | |
---|
| 680 | // Initialize search options and limits |
---|
| 681 | void initOptions() { |
---|
| 682 | // Search options |
---|
| 683 | _delta_based_restart = true; |
---|
| 684 | _restart_delta_limit = 4; |
---|
| 685 | |
---|
| 686 | // Search limits |
---|
| 687 | _iteration_limit = 1000; |
---|
| 688 | _step_limit = -1; // this is disabled by default |
---|
| 689 | _size_limit = -1; // this is disabled by default |
---|
| 690 | } |
---|
[999] | 691 | |
---|
| 692 | // Adds a node to the current clique |
---|
| 693 | void addCliqueNode(int u) { |
---|
| 694 | if (_clique[u]) return; |
---|
| 695 | _clique[u] = true; |
---|
| 696 | _size++; |
---|
| 697 | BoolVector &row = _gr[u]; |
---|
| 698 | for (int i = 0; i != _n; i++) { |
---|
| 699 | if (!row[i]) _delta[i]++; |
---|
| 700 | } |
---|
| 701 | } |
---|
| 702 | |
---|
| 703 | // Removes a node from the current clique |
---|
| 704 | void delCliqueNode(int u) { |
---|
| 705 | if (!_clique[u]) return; |
---|
| 706 | _clique[u] = false; |
---|
| 707 | _size--; |
---|
| 708 | BoolVector &row = _gr[u]; |
---|
| 709 | for (int i = 0; i != _n; i++) { |
---|
| 710 | if (!row[i]) _delta[i]--; |
---|
| 711 | } |
---|
| 712 | } |
---|
| 713 | |
---|
| 714 | // Initialize data structures |
---|
| 715 | void init() { |
---|
| 716 | _n = countNodes(_graph); |
---|
| 717 | int ui = 0; |
---|
| 718 | for (NodeIt u(_graph); u != INVALID; ++u) { |
---|
| 719 | _id[u] = ui++; |
---|
| 720 | } |
---|
| 721 | _gr.clear(); |
---|
| 722 | _gr.resize(_n, BoolVector(_n, false)); |
---|
| 723 | ui = 0; |
---|
| 724 | for (NodeIt u(_graph); u != INVALID; ++u) { |
---|
| 725 | for (IncEdgeIt e(_graph, u); e != INVALID; ++e) { |
---|
| 726 | int vi = _id[_graph.runningNode(e)]; |
---|
| 727 | _gr[ui][vi] = true; |
---|
| 728 | _gr[vi][ui] = true; |
---|
| 729 | } |
---|
| 730 | ++ui; |
---|
| 731 | } |
---|
| 732 | |
---|
| 733 | _clique.clear(); |
---|
| 734 | _clique.resize(_n, false); |
---|
| 735 | _size = 0; |
---|
| 736 | _best_clique.clear(); |
---|
| 737 | _best_clique.resize(_n, false); |
---|
| 738 | _best_size = 0; |
---|
| 739 | _delta.clear(); |
---|
| 740 | _delta.resize(_n, 0); |
---|
| 741 | _tabu.clear(); |
---|
| 742 | _tabu.resize(_n, false); |
---|
| 743 | } |
---|
| 744 | |
---|
| 745 | // Executes the algorithm |
---|
| 746 | template <typename SelectionRuleImpl> |
---|
[1022] | 747 | TerminationCause start() { |
---|
| 748 | if (_n == 0) return SIZE_LIMIT; |
---|
[999] | 749 | if (_n == 1) { |
---|
| 750 | _best_clique[0] = true; |
---|
| 751 | _best_size = 1; |
---|
[1022] | 752 | return SIZE_LIMIT; |
---|
[999] | 753 | } |
---|
| 754 | |
---|
[1022] | 755 | // Iterated local search algorithm |
---|
| 756 | const int max_size = _size_limit >= 0 ? _size_limit : _n; |
---|
| 757 | const int max_restart = _iteration_limit >= 0 ? |
---|
| 758 | _iteration_limit : std::numeric_limits<int>::max(); |
---|
| 759 | const int max_select = _step_limit >= 0 ? |
---|
| 760 | _step_limit : std::numeric_limits<int>::max(); |
---|
| 761 | |
---|
[999] | 762 | SelectionRuleImpl sel_method(*this); |
---|
[1022] | 763 | int select = 0, restart = 0; |
---|
[999] | 764 | IntVector restart_nodes; |
---|
[1022] | 765 | while (select < max_select && restart < max_restart) { |
---|
[999] | 766 | |
---|
| 767 | // Perturbation/restart |
---|
[1022] | 768 | restart++; |
---|
| 769 | if (_delta_based_restart) { |
---|
[999] | 770 | restart_nodes.clear(); |
---|
| 771 | for (int i = 0; i != _n; i++) { |
---|
[1022] | 772 | if (_delta[i] >= _restart_delta_limit) |
---|
[999] | 773 | restart_nodes.push_back(i); |
---|
| 774 | } |
---|
| 775 | } |
---|
| 776 | int rs_node = -1; |
---|
| 777 | if (restart_nodes.size() > 0) { |
---|
| 778 | rs_node = restart_nodes[_rnd[restart_nodes.size()]]; |
---|
| 779 | } else { |
---|
| 780 | rs_node = _rnd[_n]; |
---|
| 781 | } |
---|
| 782 | BoolVector &row = _gr[rs_node]; |
---|
| 783 | for (int i = 0; i != _n; i++) { |
---|
| 784 | if (_clique[i] && !row[i]) delCliqueNode(i); |
---|
| 785 | } |
---|
| 786 | addCliqueNode(rs_node); |
---|
| 787 | |
---|
| 788 | // Local search |
---|
| 789 | _tabu.clear(); |
---|
| 790 | _tabu.resize(_n, false); |
---|
| 791 | bool tabu_empty = true; |
---|
| 792 | int max_swap = _size; |
---|
| 793 | while (select < max_select) { |
---|
| 794 | select++; |
---|
| 795 | int u; |
---|
| 796 | if ((u = sel_method.nextFeasibleAddNode()) != -1) { |
---|
| 797 | // Feasible add move |
---|
| 798 | addCliqueNode(u); |
---|
| 799 | if (tabu_empty) max_swap = _size; |
---|
| 800 | } |
---|
| 801 | else if ((u = sel_method.nextFeasibleSwapNode()) != -1) { |
---|
| 802 | // Feasible swap move |
---|
| 803 | int v = -1; |
---|
| 804 | BoolVector &row = _gr[u]; |
---|
| 805 | for (int i = 0; i != _n; i++) { |
---|
| 806 | if (_clique[i] && !row[i]) { |
---|
| 807 | v = i; |
---|
| 808 | break; |
---|
| 809 | } |
---|
| 810 | } |
---|
| 811 | addCliqueNode(u); |
---|
| 812 | delCliqueNode(v); |
---|
| 813 | _tabu[v] = true; |
---|
| 814 | tabu_empty = false; |
---|
| 815 | if (--max_swap <= 0) break; |
---|
| 816 | } |
---|
| 817 | else if ((u = sel_method.nextAddNode()) != -1) { |
---|
| 818 | // Non-feasible add move |
---|
| 819 | addCliqueNode(u); |
---|
| 820 | } |
---|
| 821 | else break; |
---|
| 822 | } |
---|
| 823 | if (_size > _best_size) { |
---|
| 824 | _best_clique = _clique; |
---|
| 825 | _best_size = _size; |
---|
[1022] | 826 | if (_best_size >= max_size) return SIZE_LIMIT; |
---|
[999] | 827 | } |
---|
| 828 | sel_method.update(); |
---|
| 829 | } |
---|
| 830 | |
---|
[1022] | 831 | return (restart >= max_restart ? ITERATION_LIMIT : STEP_LIMIT); |
---|
[999] | 832 | } |
---|
| 833 | |
---|
| 834 | }; //class GrossoLocatelliPullanMc |
---|
| 835 | |
---|
| 836 | ///@} |
---|
| 837 | |
---|
| 838 | } //namespace lemon |
---|
| 839 | |
---|
| 840 | #endif //LEMON_GROSSO_LOCATELLI_PULLAN_MC_H |
---|