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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 Parallel Variable Annealing Length (P-VAL) restart schedule originated, as you would expect
 * from the word "annealing" in its name, as a restart schedule for Simulated Annealing.
 * Specifically, it is a parallel version of the Variable Annealing Length (VAL) restart schedule.
 * Its intended use is to schedule the run lengths for restarts across a set of parallel multistart
 * metaheuristics. See the {@link VariableAnnealingLength} class for the sequential version of this
 * restart schedule.
 *
 * The motivation underlying P-VAL 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 sequential version of the restart schedule, known as VAL, 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, ....
 *
 * 
P-VAL is a parallel version of VAL. Its essence is the same sequence of restarts, but those
 * restart run lengths are spread across a maximum of 4 parallel instances of the search. If there
 * are more than 4 parallel instances, the schedule repeats with more than one search following the
 * same schedule. The run length, r_i,t, of restart i of thread t is as follows:
 * r_i,t = 1000 * 2^{(t mod 4) + i*min(4,N)}, where i begins at 0, N is the number
 * of parallel search instances (i.e., threads), and the thread id t is an integer in [0, N). Note
 * that we do not implement this with an actual exponentiation. Rather, each run length of the
 * sequence is computed incrementally from the prior run length. Here are a few examples:
 *
 * 
Example 1 (N=3): Thread 0 follows the schedule of run lengths: 1000, 8000, 64000, .... Thread
 * 1 follows the schedule of run lengths: 2000, 16000, 128000, .... Thread 2 follows the schedule of
 * run lengths: 4000, 32000, 256000, ....
 *
 * 
Example 2 (N=4): Thread 0 follows the schedule of run lengths: 1000, 16000, 256000, ....
 * Thread 1 follows the schedule of run lengths: 2000, 32000, 512000, .... Thread 2 follows the
 * schedule of run lengths: 4000, 64000, 1024000, .... Thread 3 follows the schedule of run lengths:
 * 8000, 128000, 2048000, ....
 *
 * 
Example 3 (N>4): Thread 0 follows the schedule of run lengths: 1000, 16000, 256000, ....
 * Thread 1 follows the schedule of run lengths: 2000, 32000, 512000, .... Thread 2 follows the
 * schedule of run lengths: 4000, 64000, 1024000, .... Thread 3 follows the schedule of run lengths:
 * 8000, 128000, 2048000, .... Thread t, where t≥4, follows the same schedule of run lengths as
 * thread (t-4).
 *
 * 
See the original publication, referenced below, for the theoretical rationale for beginning
 * the schedule fresh every 4 threads, along with an experimental comparison between P-VAL and a
 * preliminary version referred to in that paper as P-VAL-0, which did not start anew every 4
 * threads, as validation of that theoretical result.
 *
 * 
This class supports both the original schedule as defined above, as well as including a
 * parameter to specify the initial run length r_0,0 as something other than 1000.
 *
 * 
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.
 *
 * 
Since this restart schedule assumes multiple threads, and since each thread requires its own
 * RestartSchedule object that maintains state independent of the others, we do not provide a public
 * constructor for this class. Instead, we provide a couple static factory methods, {@link
 * #createRestartSchedules(int)} and {@link #createRestartSchedules(int,int)}, each of which create
 * an array of restart schedules for the desired number of parallel instances.
 *
 * 
The P-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.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class ParallelVariableAnnealingLength implements RestartSchedule {

  private final int shift;
  private final int shiftLimit;
  private final int r0;
  private int r;

  /*
   * Constructor is private.  The factory method must be used.
   * Rationale: This restart schedule only makes sense in a parallel /
   * multithreaded scenario, and in particular only makes sense with a
   * combination of restart schedules, one per thread.
   */
  private ParallelVariableAnnealingLength(int shift, int r0) {
    r = this.r0 = r0;
    this.shift = shift;
    shiftLimit = 0x40000000 >> (shift - 1);
  }

  /*
   * Copy constructor is private.  The factory method must be used.
   * Rationale: This restart schedule only makes sense in a parallel /
   * multithreaded scenario, and in particular only makes sense with a
   * combination of restart schedules, one per thread.
   */
  private ParallelVariableAnnealingLength(ParallelVariableAnnealingLength other) {
    r = r0 = other.r0;
    shift = other.shift;
    shiftLimit = other.shiftLimit;
  }

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

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

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

  /**
   * Creates a list of restart schedules that together follow the Parallel Variable Annealing Length
   * (P-VAL) 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.
   *
   * 
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. This
   * factory method ensures that the combination of individual restart schedules together conforms
   * to the parallel restart schedule known as P-VAL.
   *
   * @param numThreads The number of parallel instances of the search.
   * @return The P-VAL restart schedule as a list of separate restart schedules, one for each
   *     desired parallel instance. The combination of restart schedules together implement P-VAL.
   * @throws IllegalArgumentException if numThreads ≤ 0.
   */
  public static List createRestartSchedules(int numThreads) {
    return createRestartSchedules(numThreads, 1000);
  }

  /**
   * Creates a list of restart schedules that together follow the Parallel Variable Annealing Length
   * (P-VAL) 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.
   *
   * 
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. This
   * factory method ensures that the combination of individual restart schedules together conforms
   * to the parallel restart schedule known as P-VAL.
   *
   * This factory method is a mild modification of the original P-VAL restart schedule.
   * Specifically, the original schedule sets the shortest run length of any of the parallel search
   * instances at 1000, while this factory method enables the programmer to specify this. The
   * original run length of 1000 is appropriate for simulated annealing, however, for other
   * metaheuristics you may have reason to initialize the schedule with either shorter or longer
   * runs.
   *
   * @param numThreads The number of parallel instances of the search.
   * @param r0 The shortest run length of any of the parallel instances.
   * @return The P-VAL restart schedule as a list of separate restart schedules, one for each
   *     desired parallel instance. The combination of restart schedules together implement P-VAL.
   * @throws IllegalArgumentException if numThreads ≤ 0 or if r0 ≤ 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);
    int shift = numThreads < 4 ? numThreads : 4;
    for (int i = 0; i < shift; i++) {
      schedules.add(new ParallelVariableAnnealingLength(shift, r0));
      r0 = r0 << 1;
    }
    for (int i = shift; i < numThreads; i++) {
      schedules.add(new ParallelVariableAnnealingLength(schedules.get(i - 4)));
    }
    return schedules;
  }
}