org.cicirello.search.ss.Partial Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of chips-n-salsa Show documentation

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.

There is a newer version: 7.0.1

Show newest version

/*
 * 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 org.cicirello.util.Copyable;

/**
 * A Partial represents a partial solution to a problem (e.g., a partial permutation or a partial
 * integer vector) that is being iteratively constructed as a solution to an optimization problem.
 * This class supports the implementation of constructive heuristics for optimization problems, as
 * well as for stochastic sampling algorithms that rely on constructive heuristics.
 *
 * @param  The type of object that this Partial can become, which is assumed to be an object that
 *     is a sequence of integers (e.g., vector of integers, permutation, or some other indexable
 *     type that stores integers).
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public interface Partial> {

  /**
   * Generates a complete instance that is consistent with this Partial. That is, elements already
   * added to the Partial will retain their positions, while elements not already in the Partial
   * will be added in an undefined order such that the result is a valid complete object of type T.
   *
   * @return a valid object of type T consistent with the current state of this Partial
   */
  T toComplete();

  /**
   * Checks if the Partial is actually a complete T.
   *
   * @return true if the Partial is a complete T.
   */
  boolean isComplete();

  /**
   * Gets the element in the Partial at position index.
   *
   * @param index The position, which must be less than size(). The valid index values are [0, 1,
   *     ..., (size()-1)].
   * @return the element in position index.
   * @throws ArrayIndexOutOfBoundsException if index is greater than or equal to size(), or if index
   *     is less than 0
   */
  int get(int index);

  /**
   * Gets the element in the current last position of the Partial, i.e., the element at index:
   * size()-1.
   *
   * @return The element in the current last position of the Partial.
   * @throws ArrayIndexOutOfBoundsException if size() is 0
   */
  int getLast();

  /**
   * Gets the size of the Partial, which is the number of elements that have already been added to
   * it. Note that this is NOT the size of the final complete T. Rather, it is the current size of
   * the Partial.
   *
   * @return size The size of the Partial.
   */
  int size();

  /**
   * Gets the number of elements not yet added to the Partial. We refer to these as extensions since
   * adding an element will extend the size of the Partial.
   *
   * @return the number of elements not yet added to the Partial
   */
  int numExtensions();

  /**
   * Gets the element in position index of the list of possible extensions. This method gets the
   * element at position index from the list of those elements that can be added next to the
   * Partial. For example, if this is a partial permutation, then this would get one of the elements
   * not yet added to the permutation. Or for example, if this is a partial vector of integers, then
   * this would get one of the values that is allowed to be added to the next position of the
   * vector. Note that each time {@link #extend} is called that the remaining elements may be
   * reordered, so you cannot assume that the extensions remain in the same positions once you call
   * extend.
   *
   * @param extensionIndex An index into the list of elements that can be added to the Partial next.
   *     The valid extensionIndex values are [0, 1, ..., (numExtensions()-1)].
   * @return the element at the designated index in the list of elements that can be added to the
   *     Partial.
   * @throws ArrayIndexOutOfBoundsException if extensionIndex is greater than or equal to
   *     numExtensions(), or if extensionIndex is less than 0
   */
  int getExtension(int extensionIndex);

  /**
   * Extends the Partial by adding an element to the end of the Partial. If size() is the size of
   * the Partial before the extension, then the new element is added to position size() and the
   * size() is increased by 1.
   *
   * @param extensionIndex An index into the list of elements that can be added to the Partial. The
   *     valid extensionIndex values are [0, 1, ..., (numExtensions()-1)].
   * @throws ArrayIndexOutOfBoundsException if extensionIndex is greater than or equal to
   *     numExtensions(), or if extensionIndex is less than 0
   */
  void extend(int extensionIndex);
}