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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.restarts;

import java.util.ArrayList;
import java.util.List;

/**
 * The Variable Annealing Length (VAL) restart schedule originated, as you would expect from the
 * word "annealing" in its name, as a restart schedule for Simulated Annealing. Its motivation is
 * two-fold. First, a commonly encountered observation is that a single long run of simulated
 * annealing usually outperforms multiple short runs whose combined length is that of the long run
 * (assuming the annealing schedule is tuned well). Second, it is often the case that we don't know
 * beforehand how long of a run we have time to execute, thus our annealing schedule may not be
 * tuned properly for our available time (e.g., we may cool too quickly or too slowly).
 *
 * The VAL restart schedule starts with a short run, and increases run length exponentially
 * across restarts. Specifically, define r_i as the run length for run i, with the
 * following: r_i = 1000 * 2ⁱ. For simulated annealing, run length is number of
 * evaluations (i.e., iterations of the simulated annealing main loop). You can compute the sequence
 * of run lengths incrementally with r₀ = 1000 and r_i = 2r_i-1. The
 * first few run lengths in the sequence are: 1000, 2000, 4000, ....
 *
 * 
The VAL restart schedule was introduced in:

 * Vincent A. Cicirello. "Variable Annealing Length and Parallelism in Simulated Annealing." In
 * Proceedings of the Tenth International Symposium on Combinatorial Search (SoCS 2017), pages 2-10.
 * AAAI Press, June 2017.
 *
 * 
This class supports both the original schedule as defined above, as well as including a
 * parameter to specify the initial run length r₀ as something other than 1000. In this
 * case, the subsequent run lengths are still twice the previous. For example, if you start
 * r₀ = 50, then the run lengths will follow the sequence: 50, 100, 200, 400, ....
 *
 * 
Although not originally stated in the paper that proposed this restart schedule, this
 * implementation converges to a constant restart length of Integer.MAX_VALUE if the next run length
 * of the schedule would otherwise exceed the maximum positive 32-bit integer value.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 * @version 1.25.2021
 */
public final class VariableAnnealingLength implements RestartSchedule {

  private final int r0;
  private int r;

  /**
   * The default constructor constructs the original Variable Annealing Length (VAL) restart
   * schedule of: Vincent A. Cicirello. "Variable Annealing Length and Parallelism in Simulated
   * Annealing." In Proceedings of the Tenth International Symposium on Combinatorial Search (SoCS
   * 2017), pages 2-10. AAAI Press, June 2017. Specifically, the initial run length is 1000, and
   * each subsequent run length is twice the previous.
   */
  public VariableAnnealingLength() {
    r = r0 = 1000;
  }

  /**
   * This constructor enables specifying the initial run length for the first run. Subsequent runs
   * otherwise follow the original schedule and are twice the length of the previous run. This
   * restart schedule originated with simulated annealing, but when used with other metaheuristics
   * it may be desirable to either increase or decrease the initial run length from what was
   * originally used.
   *
   * @param r0 The initial run length for the first run.
   */
  public VariableAnnealingLength(int r0) {
    if (r0 < 1) throw new IllegalArgumentException("r0 must be positive");
    r = this.r0 = r0;
  }

  @Override
  public int nextRunLength() {
    int next = r;
    if (r < 0x40000000) r = r << 1;
    else r = 0x7fffffff;
    return next;
  }

  @Override
  public void reset() {
    r = r0;
  }

  @Override
  public VariableAnnealingLength split() {
    return new VariableAnnealingLength(r0);
  }

  /**
   * This is a convenience method for use in generating several identical VAL annealing schedules,
   * such as if needed for a parallel search. All of the annealing schedules in the returned list
   * are identical, but are independent (no shared state). This does NOT give you the P-VAL schedule
   * (see {@link ParallelVariableAnnealingLength} for the P-VAL schedule).
   *
   * 
The list that is returned is of the size of the requested number of threads. This should
   * correspond to the number of parallel instances of the search you intend to execute.
   *
   * @param numThreads The number of parallel instances of the search.
   * @return A list of numThreads identical VAL restart schedules.
   * @throws IllegalArgumentException if numThreads ≤ 0.
   */
  public static List createRestartSchedules(int numThreads) {
    return createRestartSchedules(numThreads, 1000);
  }

  /**
   * This is a convenience method for use in generating several identical VAL annealing schedules,
   * such as if needed for a parallel search. All of the annealing schedules in the returned list
   * are identical, but are independent (no shared state). This does NOT give you the P-VAL schedule
   * (see {@link ParallelVariableAnnealingLength} for the P-VAL schedule).
   *
   * The list that is returned is of the size of the requested number of threads. This should
   * correspond to the number of parallel instances of the search you intend to execute.
   *
   * @param numThreads The number of parallel instances of the search.
   * @param r0 The initial run length for the first run.
   * @return A list of numThreads identical VAL restart schedules.
   * @throws IllegalArgumentException if numThreads ≤ 0.
   */
  public static List createRestartSchedules(int numThreads, int r0) {
    if (numThreads <= 0) throw new IllegalArgumentException("Must have at least 1 thread.");
    if (r0 <= 0) throw new IllegalArgumentException("r0 must be greater than 0");
    ArrayList schedules =
        new ArrayList(numThreads);
    for (int i = 0; i < numThreads; i++) {
      schedules.add(new VariableAnnealingLength(r0));
    }
    return schedules;
  }
}