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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.sa;

import java.util.concurrent.ThreadLocalRandom;

/**
 * This class implements the classic and most commonly encountered cooling schedule for simulated
 * annealing, the annealing schedule known as exponential cooling (sometimes referred to as
 * geometric cooling). In this cooling schedule, the k-th temperature, t_k, is determined
 * as follows: t_k = α^k * t₀, where t₀ is the
 * initial temperature and α is the cooling rate. The new temperature is usually computed
 * incrementally from the previous with: t_k = α * t_k-1. In some
 * applications, the temperature update occurs with each simulated annealing evaluation, while in
 * others it is updated periodically, such as every s steps (i.e., iterations) of simulated
 * annealing. This class supports this periodic update approach, with a default of every step. See
 * the parameters of the constructors for more information.
 *
 * Additionally, this class stops updating the temperature once it is less than or equal to
 * 0.001. For any foreseeable cost scale that a problem may have, a temperature value of 0.001 is
 * sufficiently low such that all moves that are worse than the current state will be rejected, so
 * further cooling would be superfluous.
 *
 * The {@link #accept accept} methods of this class use the classic, and most common, Boltzmann
 * distribution for determining whether to accept a neighbor.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class ExponentialCooling implements AnnealingSchedule {

  private double t;
  private final double t0;
  private final double alpha;
  private final int steps;
  private int stepCounter;

  /**
   * Constructs an exponential cooling schedule for simulated annealing.
   *
   * @param t0 The initial temperature for the start of an annealing run. The value of t0 must be
   *     positive.
   * @param alpha The cooling rate. Each time the temperature is cooled, it is cooled as follows: t
   *     = t * alpha. The value of alpha must be greater than 0 and less than 1.
   * @param steps The number of iterations of simulated annealing between cooling events. Steps must
   *     be positive. If 0 or a negative is passed for steps, steps is set to 1.
   * @throws IllegalArgumentException if t0 ≤ 0 or alpha ≤ 0 or alpha ≥ 1.
   */
  public ExponentialCooling(double t0, double alpha, int steps) {
    if (t0 <= 0) throw new IllegalArgumentException("Initial temperature must be positive");
    if (alpha <= 0 || alpha >= 1)
      throw new IllegalArgumentException("alpha must be in interval (0,1)");
    t = this.t0 = t0;
    this.alpha = alpha;
    this.steps = steps <= 0 ? 1 : steps;
  }

  /**
   * Constructs an exponential cooling schedule for simulated annealing.
   *
   * @param t0 The initial temperature for the start of an annealing run. The value of t0 must be
   *     positive.
   * @param alpha The cooling rate. During each iteration of simulated annealing, the temperature is
   *     cooled as follows: t = t * alpha. The value of alpha must be greater than 0 and less than
   *     1.
   * @throws IllegalArgumentException if t0 ≤ 0 or alpha ≤ 0 or alpha ≥ 1.
   */
  public ExponentialCooling(double t0, double alpha) {
    if (t0 <= 0) throw new IllegalArgumentException("Initial temperature must be positive");
    if (alpha <= 0 || alpha >= 1)
      throw new IllegalArgumentException("alpha must be in interval (0,1)");
    t = this.t0 = t0;
    this.alpha = alpha;
    this.steps = 1;
  }

  /*
   * private copy constructor for internal use only
   */
  private ExponentialCooling(ExponentialCooling other) {
    t = t0 = other.t0;
    alpha = other.alpha;
    steps = other.steps;
  }

  @Override
  public void init(int maxEvals) {
    t = t0;
    stepCounter = 0;
  }

  @Override
  public boolean accept(double neighborCost, double currentCost) {
    boolean doAccept =
        neighborCost <= currentCost
            || ThreadLocalRandom.current().nextDouble()
                < Math.exp((currentCost - neighborCost) / t);
    stepCounter++;
    if (stepCounter == steps && t > 0.001) {
      stepCounter = 0;
      t *= alpha;
    }
    return doAccept;
  }

  @Override
  public ExponentialCooling split() {
    return new ExponentialCooling(this);
  }

  /*
   * package-private for unit testing
   */
  double getTemperature() {
    return t;
  }
}