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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.ss;

import java.util.Arrays;
import org.cicirello.search.representations.BoundedIntegerVector;
import org.cicirello.search.representations.IntegerVector;

/**
 * A PartialIntegerVector represents a vector of integers that is being iteratively constructed as a
 * solution to an optimization problem over the space of integer vectors. This class supports the
 * implementation of constructive heuristics for optimization problems where solutions are
 * represented as a vector of integers, as well as for stochastic sampling algorithms that rely on
 * constructive heuristics.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class PartialIntegerVector implements Partial {

  private final int min;
  private final int max;
  private int extendCount;
  private final boolean enforceBounds;
  private final int[] partial;
  private int size;

  /**
   * Constructs a PartialIntegerVector of a specified length with specified min and max values for
   * the integers it contains. The default behavior is for the final IntegerVector that is generated
   * by this PartialIntegerVector to continue to enforce those bounds.
   *
   * @param n The desired length of the final IntegerVector, which must be non-negative.
   * @param min The minimum value for any individual integer in the vector.
   * @param max The maximum value for any individual integer in the vector.
   * @throws IllegalArgumentException if n is less than 0
   * @throws IllegalArgumentException if min is greater than max
   */
  public PartialIntegerVector(int n, int min, int max) {
    this(n, min, max, true);
  }

  /**
   * Constructs a PartialIntegerVector.
   *
   * @param n The desired length of the final IntegerVector, which must be non-negative.
   * @param min The minimum value for any individual integer in the vector.
   * @param max The maximum value for any individual integer in the vector.
   * @param enforceBounds If true, then the final IntegerVector generated by this
   *     PartialIntegerVector will continue to enforce the min and max bounds during subsequent
   *     changes.
   * @throws IllegalArgumentException if n is less than 0
   * @throws IllegalArgumentException if min is greater than max
   */
  public PartialIntegerVector(int n, int min, int max, boolean enforceBounds) {
    if (n < 0) throw new IllegalArgumentException("n must not be negative");
    if (min > max) throw new IllegalArgumentException("min must be less than or equal to max");
    this.min = min;
    this.max = max;
    extendCount = n > 0 ? max - min + 1 : 0;
    this.enforceBounds = enforceBounds;
    partial = new int[n];
    // deliberately using default of size=0
  }

  @Override
  public IntegerVector toComplete() {
    if (size < partial.length) {
      Arrays.fill(partial, size, partial.length, min);
    }
    return enforceBounds ? new BoundedIntegerVector(partial, min, max) : new IntegerVector(partial);
  }

  @Override
  public boolean isComplete() {
    return size == partial.length;
  }

  @Override
  public int get(int index) {
    if (index >= size) {
      throw new ArrayIndexOutOfBoundsException("index must be less than size");
    }
    return partial[index];
  }

  @Override
  public int getLast() {
    return partial[size - 1];
  }

  @Override
  public int size() {
    return size;
  }

  @Override
  public int numExtensions() {
    return extendCount;
  }

  @Override
  public int getExtension(int extensionIndex) {
    if (extensionIndex >= extendCount) {
      throw new ArrayIndexOutOfBoundsException("extensionIndex must be less than numExtensions()");
    }
    return min + extensionIndex;
  }

  @Override
  public void extend(int extensionIndex) {
    if (extensionIndex >= extendCount) {
      throw new ArrayIndexOutOfBoundsException("extensionIndex must be less than numExtensions()");
    }
    partial[size] = min + extensionIndex;
    size++;
    if (size == partial.length) extendCount = 0;
  }
}