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Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

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/*
 * Chips-n-Salsa: A library of parallel self-adaptive local search algorithms.
 * Copyright (C) 2002-2022 Vincent A. Cicirello
 *
 * This file is part of Chips-n-Salsa (https://chips-n-salsa.cicirello.org/).
 *
 * Chips-n-Salsa is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Chips-n-Salsa is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package org.cicirello.search.problems.tsp;

import java.util.SplittableRandom;
import java.util.random.RandomGenerator;
import org.cicirello.math.rand.RandomIndexer;
import org.cicirello.permutations.Permutation;
import org.cicirello.search.problems.IntegerCostOptimizationProblem;
import org.cicirello.search.problems.OptimizationProblem;

/**
 * This class and its nested classes implement the Traveling Salesperson Problem (TSP), and its
 * variant, the Asymmetric Traveling Salesperson Problem (ATSP), by generating a random distance
 * matrix. The RandomTSPMatrix class provides two inner classes, one for edge costs that are
 * floating-point valued (class {@link Double}), and one for integer cost edges (class {@link
 * Integer}). Both nested classes support both the TSP and ATSP, and both also provide the option to
 * control whether or not the distance matrix satisfies the triangle inequality.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public abstract class RandomTSPMatrix extends BaseTSP {

  /*
   * package private constructor for use by subclasses only
   */
  RandomTSPMatrix() {}

  /**
   * This class implements the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric
   * Traveling Salesperson Problem (ATSP), by generating a random distance matrix, with integer cost
   * edges. It supports both the TSP and ATSP, and also provides the option to control whether or
   * not the distance matrix satisfies the triangle inequality.
   *
   * The random distance matrix is generated via an approach based on that of the paper:
   * Cirasella J., Johnson D.S., McGeoch L.A., Zhang W. (2001) The Asymmetric Traveling Salesman
   * Problem: Algorithms, Instance Generators, and Tests. In Algorithm Engineering and
   * Experimentation (ALENEX 2001). There are some minor differences between the approach
   * described in that paper and the approach of this class. This class generates random distances
   * with a minimum of 1, whereas the approach described in that paper allows distances of 0.
   *
   * @author Vincent A. Cicirello, https://www.cicirello.org/
   */
  public static final class Integer extends RandomTSPMatrix
      implements IntegerCostOptimizationProblem {

    private final int[][] d;

    /**
     * Generates a random instance of either the TSP. The instances generated by this constructor
     * may not satisfy the triangle inequality. If you desire an instance that satisfies the
     * triangle inequality, see the other constructors. The distance matrix generated by this
     * constructor is symmetric. See the other constructors for the ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [1, maxDistance].
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 1.
     */
    public Integer(int n, int maxDistance) {
      this(n, maxDistance, true, false);
    }

    /**
     * Generates a random instance of either the TSP or ATSP. The instances generated by this
     * constructor may not satisfy the triangle inequality. If you desire an instance that satisfies
     * the triangle inequality, see the other constructors.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [1, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 1.
     */
    public Integer(int n, int maxDistance, boolean symmetric) {
      this(n, maxDistance, symmetric, false);
    }

    /**
     * Generates a random instance of either the TSP or ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [1, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @param triangleInequality Pass true if you want the generated distance matrix to respect the
     *     triangle inequality, or false for purely random distances.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 1.
     */
    public Integer(int n, int maxDistance, boolean symmetric, boolean triangleInequality) {
      this(n, maxDistance, symmetric, triangleInequality, new SplittableRandom());
    }

    /**
     * Generates a random instance of either the TSP or ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [1, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @param triangleInequality Pass true if you want the generated distance matrix to respect the
     *     triangle inequality, or false for purely random distances.
     * @param seed The seed for the random number generator to enable reproducing the same instance
     *     for experiment reproducibility.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 1.
     */
    public Integer(
        int n, int maxDistance, boolean symmetric, boolean triangleInequality, long seed) {
      this(n, maxDistance, symmetric, triangleInequality, new SplittableRandom(seed));
    }

    /**
     * Although the focus of this class is generating random TSP and ATSP instances, this
     * constructor enables specifying the distance matrix directly.
     *
     * @param distance The distance matrix, such that distance[i][j] is the distance from city i to
     *     city j. The distance matrix must be square, with same number of rows as columns, and
     *     whose dimension determines number of cities. Dimensions must be at least 2 by 2.
     * @throws IllegalArgumentException if distance is not at least a 2 by 2 array.
     * @throws IllegalArgumentException if number of rows is not same as number of columns, or if
     *     rows don't all have same length.
     */
    public Integer(int[][] distance) {
      final int n = distance.length;
      if (n < 2) throw new IllegalArgumentException("distance must be at least 2 by 2");
      d = new int[n][n];
      for (int i = 0; i < n; i++) {
        if (distance[i].length != n) {
          throw new IllegalArgumentException("num rows and columns must be the same");
        }
        System.arraycopy(distance[i], 0, d[i], 0, n);
      }
    }

    /*
     * internal private constructor
     */
    private Integer(
        int n,
        int maxDistance,
        boolean symmetric,
        boolean triangleInequality,
        RandomGenerator gen) {
      if (n < 2) throw new IllegalArgumentException("n must be at least 2");
      if (maxDistance < 1) throw new IllegalArgumentException("maxDistance must be at least 1");
      d = new int[n][n];
      if (symmetric) {
        symmetricInitD(maxDistance, gen);
        if (triangleInequality) {
          symmetricCloseUnderShortestPaths();
        }
      } else {
        asymmetricInitD(maxDistance, gen);
        if (triangleInequality) {
          asymmetricCloseUnderShortestPaths();
        }
      }
    }

    /**
     * Gets the distance (i.e., cost) of an edge.
     *
     * @param i The source city.
     * @param j The destination city.
     * @return The distance from i to j
     */
    public final int getDistance(int i, int j) {
      return d[i][j];
    }

    /**
     * Gets the number of cities in the TSP instance.
     *
     * @return number of cities
     */
    @Override
    public final int length() {
      return d.length;
    }

    @Override
    public int cost(Permutation candidate) {
      int total = d[candidate.get(candidate.length() - 1)][candidate.get(0)];
      for (int k = 1; k < candidate.length(); k++) {
        total = total + d[candidate.get(k - 1)][candidate.get(k)];
      }
      return total;
    }

    @Override
    public int value(Permutation candidate) {
      return cost(candidate);
    }

    @Override
    public int minCost() {
      return 0;
    }

    /*
     * package private to support implementing heuristics in same package.
     */
    @Override
    final double edgeCostForHeuristics(int i, int j) {
      return d[i][j];
    }

    private void symmetricInitD(int maxDistance, RandomGenerator gen) {
      for (int i = 0; i < d.length; i++) {
        for (int j = i + 1; j < d.length; j++) {
          d[i][j] = d[j][i] = 1 + RandomIndexer.nextInt(maxDistance, gen);
        }
      }
    }

    private void asymmetricInitD(int maxDistance, RandomGenerator gen) {
      for (int i = 0; i < d.length; i++) {
        for (int j = 0; j < d.length; j++) {
          if (i != j) {
            d[i][j] = 1 + RandomIndexer.nextInt(maxDistance, gen);
          }
        }
      }
    }

    private void symmetricCloseUnderShortestPaths() {
      boolean changed = true;
      while (changed) {
        changed = false;
        for (int i = 0; i < d.length; i++) {
          for (int j = i + 1; j < d.length; j++) {
            for (int k = 0; k < d.length; k++) {
              if (k != i && k != j) {
                int sum = d[i][k] + d[k][j];
                if (d[i][j] > sum) {
                  d[i][j] = d[j][i] = sum;
                  changed = true;
                }
              }
            }
          }
        }
      }
    }

    private void asymmetricCloseUnderShortestPaths() {
      boolean changed = true;
      while (changed) {
        changed = false;
        for (int i = 0; i < d.length; i++) {
          for (int j = 0; j < d.length; j++) {
            if (i != j) {
              for (int k = 0; k < d.length; k++) {
                if (k != i && k != j) {
                  int sum = d[i][k] + d[k][j];
                  if (d[i][j] > sum) {
                    d[i][j] = sum;
                    changed = true;
                  }
                }
              }
            }
          }
        }
      }
    }
  }

  /**
   * This class implements the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric
   * Traveling Salesperson Problem (ATSP), by generating a random distance matrix, with
   * floating-point cost edges. It supports both the TSP and ATSP, and also provides the option to
   * control whether or not the distance matrix satisfies the triangle inequality.
   *
   * The random distance matrix is generated via an approach based on that of the paper:
   * Cirasella J., Johnson D.S., McGeoch L.A., Zhang W. (2001) The Asymmetric Traveling Salesman
   * Problem: Algorithms, Instance Generators, and Tests. In Algorithm Engineering and
   * Experimentation (ALENEX 2001). There are some minor differences between the approach
   * described in that paper and the approach of this class. This class generates random
   * floating-point distances, whereas the approach described in that paper uses integer valued
   * distances.
   *
   * @author Vincent A. Cicirello, https://www.cicirello.org/
   */
  public static final class Double extends RandomTSPMatrix
      implements OptimizationProblem {

    private final double[][] d;

    /**
     * Generates a random instance of either the TSP. The instances generated by this constructor
     * may not satisfy the triangle inequality. If you desire an instance that satisfies the
     * triangle inequality, see the other constructors. The distance matrix generated by this
     * constructor is symmetric. See the other constructors for the ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [0, maxDistance].
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 0.0.
     */
    public Double(int n, double maxDistance) {
      this(n, maxDistance, true, false);
    }

    /**
     * Generates a random instance of either the TSP or ATSP. The instances generated by this
     * constructor may not satisfy the triangle inequality. If you desire an instance that satisfies
     * the triangle inequality, see the other constructors.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [0, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 0.0.
     */
    public Double(int n, double maxDistance, boolean symmetric) {
      this(n, maxDistance, symmetric, false);
    }

    /**
     * Generates a random instance of either the TSP or ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [0, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @param triangleInequality Pass true if you want the generated distance matrix to respect the
     *     triangle inequality, or false for purely random distances.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 0.0.
     */
    public Double(int n, double maxDistance, boolean symmetric, boolean triangleInequality) {
      this(n, maxDistance, symmetric, triangleInequality, new SplittableRandom());
    }

    /**
     * Generates a random instance of either the TSP or ATSP.
     *
     * @param n The number of cities.
     * @param maxDistance The maximum distance between cities. The edge costs between pairs of
     *     cities are uniform in the interval [0, maxDistance].
     * @param symmetric Pass true for the TSP, or false for the ATSP.
     * @param triangleInequality Pass true if you want the generated distance matrix to respect the
     *     triangle inequality, or false for purely random distances.
     * @param seed The seed for the random number generator to enable reproducing the same instance
     *     for experiment reproducibility.
     * @throws IllegalArgumentException if n < 2.
     * @throws IllegalArgumentException if maxDistance < 0.0.
     */
    public Double(
        int n, double maxDistance, boolean symmetric, boolean triangleInequality, long seed) {
      this(n, maxDistance, symmetric, triangleInequality, new SplittableRandom(seed));
    }

    /**
     * Although the focus of this class is generating random TSP and ATSP instances, this
     * constructor enables specifying the distance matrix directly.
     *
     * @param distance The distance matrix, such that distance[i][j] is the distance from city i to
     *     city j. The distance matrix must be square, with same number of rows as columns, and
     *     whose dimension determines number of cities. Dimensions must be at least 2 by 2.
     * @throws IllegalArgumentException if distance is not at least a 2 by 2 array.
     * @throws IllegalArgumentException if number of rows is not same as number of columns, or if
     *     rows don't all have same length.
     */
    public Double(double[][] distance) {
      final int n = distance.length;
      if (n < 2) throw new IllegalArgumentException("distance must be at least 2 by 2");
      d = new double[n][n];
      for (int i = 0; i < n; i++) {
        if (distance[i].length != n) {
          throw new IllegalArgumentException("num rows and columns must be the same");
        }
        System.arraycopy(distance[i], 0, d[i], 0, n);
      }
    }

    /*
     * internal private constructor
     */
    private Double(
        int n,
        double maxDistance,
        boolean symmetric,
        boolean triangleInequality,
        RandomGenerator gen) {
      if (n < 2) throw new IllegalArgumentException("n must be at least 2");
      if (maxDistance < 0) throw new IllegalArgumentException("maxDistance must be non-negative");
      d = new double[n][n];
      if (symmetric) {
        symmetricInitD(maxDistance + Math.ulp(maxDistance), gen);
        if (triangleInequality) {
          symmetricCloseUnderShortestPaths();
        }
      } else {
        asymmetricInitD(maxDistance + Math.ulp(maxDistance), gen);
        if (triangleInequality) {
          asymmetricCloseUnderShortestPaths();
        }
      }
    }

    /**
     * Gets the distance (i.e., cost) of an edge.
     *
     * @param i The source city.
     * @param j The destination city.
     * @return The distance from i to j
     */
    public final double getDistance(int i, int j) {
      return d[i][j];
    }

    /**
     * Gets the number of cities in the TSP instance.
     *
     * @return number of cities
     */
    @Override
    public final int length() {
      return d.length;
    }

    @Override
    public double cost(Permutation candidate) {
      double total = d[candidate.get(candidate.length() - 1)][candidate.get(0)];
      for (int k = 1; k < candidate.length(); k++) {
        total = total + d[candidate.get(k - 1)][candidate.get(k)];
      }
      return total;
    }

    @Override
    public double value(Permutation candidate) {
      return cost(candidate);
    }

    @Override
    public double minCost() {
      return 0;
    }

    /*
     * package private to support implementing heuristics in same package.
     */
    @Override
    final double edgeCostForHeuristics(int i, int j) {
      return d[i][j];
    }

    private void symmetricInitD(double maxDistance, RandomGenerator gen) {
      for (int i = 0; i < d.length; i++) {
        for (int j = i + 1; j < d.length; j++) {
          d[i][j] = d[j][i] = gen.nextDouble(maxDistance);
        }
      }
    }

    private void asymmetricInitD(double maxDistance, RandomGenerator gen) {
      for (int i = 0; i < d.length; i++) {
        for (int j = 0; j < d.length; j++) {
          if (i != j) {
            d[i][j] = gen.nextDouble(maxDistance);
          }
        }
      }
    }

    private void symmetricCloseUnderShortestPaths() {
      boolean changed = true;
      while (changed) {
        changed = false;
        for (int i = 0; i < d.length; i++) {
          for (int j = i + 1; j < d.length; j++) {
            for (int k = 0; k < d.length; k++) {
              if (k != i && k != j) {
                double sum = d[i][k] + d[k][j];
                if (d[i][j] > sum) {
                  d[i][j] = d[j][i] = sum;
                  changed = true;
                }
              }
            }
          }
        }
      }
    }

    private void asymmetricCloseUnderShortestPaths() {
      boolean changed = true;
      while (changed) {
        changed = false;
        for (int i = 0; i < d.length; i++) {
          for (int j = 0; j < d.length; j++) {
            if (i != j) {
              for (int k = 0; k < d.length; k++) {
                if (k != i && k != j) {
                  double sum = d[i][k] + d[k][j];
                  if (d[i][j] > sum) {
                    d[i][j] = sum;
                    changed = true;
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}