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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) 2002-2021 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;

import org.cicirello.util.Copyable;

/**
 * An object of this class encapsulates a solution with its corresponding cost value.
 *
 * @param  The type of object the search is optimizing.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class SolutionCostPair>
    implements Comparable> {

  private final T solution;
  private final int cost;
  private final double costD;
  private final boolean containsIntCost;
  private final boolean isKnownOptimal;

  /**
   * Constructs a SolutionCostPair with integer cost.
   *
   * @param solution The solution.
   * @param cost The cost of the solution.
   * @param isKnownOptimal Pass true if this solution is known to be the optimal, and false
   *     otherwise.
   */
  public SolutionCostPair(T solution, int cost, boolean isKnownOptimal) {
    this.solution = solution;
    costD = this.cost = cost;
    containsIntCost = true;
    this.isKnownOptimal = isKnownOptimal;
  }

  /**
   * Constructs a SolutionCostPair with integer cost.
   *
   * @param solution The solution.
   * @param cost The cost of the solution.
   * @param isKnownOptimal Pass true if this solution is known to be the optimal, and false
   *     otherwise.
   */
  public SolutionCostPair(T solution, double cost, boolean isKnownOptimal) {
    this.solution = solution;
    costD = cost;
    this.cost = (int) (cost + 0.5);
    containsIntCost = false;
    this.isKnownOptimal = isKnownOptimal;
  }

  /**
   * Gets the cost contained in this solution cost pair as an int. Behavior is undefined if costs
   * are floating-point values.
   *
   * @return the cost
   */
  public int getCost() {
    return cost;
  }

  /**
   * Gets the cost contained in this solution cost pair as a double.
   *
   * @return the cost of the current best solution
   */
  public double getCostDouble() {
    return costD;
  }

  /**
   * Gets the solution in this solution cost pair.
   *
   * @return the solution
   */
  public T getSolution() {
    return solution;
  }

  /**
   * Checks whether the cost of the solution contained in this SolutionCostPair is integer valued.
   *
   * @return true if the solution has integer valued cost, and false otherwise. If this method
   *     returns false, then the behavior of the {@link #getCost} method is undefined.
   */
  public boolean containsIntCost() {
    return containsIntCost;
  }

  /**
   * Checks if the solution contained in this object has a cost value equal to the theoretical
   * minimum cost for the problem instance, such as if the cost is equal to a lower bound on the
   * cost for the problem instance.
   *
   * @return true if the solution is a known optimal, and false otherwise.
   */
  public boolean containsKnownOptimal() {
    return isKnownOptimal;
  }

  /**
   * Compares this SolutionCostPair with the specified SolutionCostPair for order. Returns a
   * negative integer, zero, or a positive integer as this SolutionCostPair has a cost that is less
   * than, equal to, or greater than the cost of the specified SolutionCostPair.
   *
   * @param other The other SolutionCostPair with which to compare.
   * @return a negative integer, zero, or a positive integer as this SolutionCostPair has a cost
   *     that is less than, equal to, or greater than the cost of the specified SolutionCostPair
   */
  @Override
  public int compareTo(SolutionCostPair other) {
    if (containsIntCost) return cost - other.cost;
    if (costD < other.costD) return -1;
    if (costD > other.costD) return 1;
    return 0;
  }
}