org.cicirello.search.ss.AbstractStochasticSampler 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.search.Metaheuristic;
import org.cicirello.search.ProgressTracker;
import org.cicirello.search.SimpleMetaheuristic;
import org.cicirello.search.SolutionCostPair;
import org.cicirello.search.problems.IntegerCostOptimizationProblem;
import org.cicirello.search.problems.OptimizationProblem;
import org.cicirello.search.problems.Problem;
import org.cicirello.util.Copyable;

/**
 * This class serves as an abstract base class for the stochastic sampling search algorithms,
 * implementing the common functionality.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
abstract class AbstractStochasticSampler>
    implements SimpleMetaheuristic, Metaheuristic {

  private final OptimizationProblem pOpt;
  private final IntegerCostOptimizationProblem pOptInt;
  private ProgressTracker tracker;
  private int numGenerated;

  /**
   * Constructs a AbstractStochasticSampler search object.
   *
   * @param problem The optimization problem to solve.
   * @param tracker A ProgressTracker
   * @throws NullPointerException if problem or tracker is null.
   */
  AbstractStochasticSampler(Problem problem, ProgressTracker tracker) {
    if (problem == null || tracker == null) {
      throw new NullPointerException();
    }
    this.tracker = tracker;
    // default: numGenerated = 0;
    if (problem instanceof IntegerCostOptimizationProblem) {
      pOptInt = (IntegerCostOptimizationProblem) problem;
      pOpt = null;
    } else {
      pOpt = (OptimizationProblem) problem;
      pOptInt = null;
    }
  }

  /*
   * package-private copy constructor in support of the split method.
   * note: copies references to thread-safe components, and splits potentially non-threadsafe components
   */
  AbstractStochasticSampler(AbstractStochasticSampler other) {
    // these are threadsafe, so just copy references
    pOpt = other.pOpt;
    pOptInt = other.pOptInt;

    // this one must be shared.
    tracker = other.tracker;

    // use default of 0 for this one: numGenerated
  }

  @Override
  public final SolutionCostPair optimize() {
    if (tracker.didFindBest() || tracker.isStopped()) return null;
    numGenerated++;
    return sample();
  }

  /**
   * Generates multiple stochastic heuristic samples. Returns the best solution of the set of
   * samples.
   *
   * @param numSamples The number of samples to perform.
   * @return The best solution of this set of samples, which may or may not be the same as the
   *     solution contained in this search's {@link org.cicirello.search.ProgressTracker
   *     ProgressTracker}, which contains the best of all runs across all calls to the various
   *     optimize methods. Returns null if no runs executed, such as if the ProgressTracker already
   *     contains the theoretical best solution.
   */
  @Override
  public final SolutionCostPair optimize(int numSamples) {
    if (tracker.didFindBest() || tracker.isStopped()) return null;
    SolutionCostPair best = null;
    for (int i = 0; i < numSamples && !tracker.didFindBest() && !tracker.isStopped(); i++) {
      SolutionCostPair current = sample();
      numGenerated++;
      if (best == null || current.compareTo(best) < 0) best = current;
    }
    return best;
  }

  @Override
  public final ProgressTracker getProgressTracker() {
    return tracker;
  }

  @Override
  public final void setProgressTracker(ProgressTracker tracker) {
    if (tracker != null) this.tracker = tracker;
  }

  @Override
  public final long getTotalRunLength() {
    return numGenerated;
  }

  @Override
  public final Problem getProblem() {
    return (pOptInt != null) ? pOptInt : pOpt;
  }

  @Override
  public abstract AbstractStochasticSampler split();

  /*
   * package-private: used internally, but want to access from test class for unit testing
   */
  final int select(double[] values, int k, double u) {
    // iterative binary search
    int first = 0;
    int last = k - 1;
    while (first < last) {
      int mid = (first + last) >> 1;
      if (u < values[mid]) {
        last = mid;
      } else {
        first = mid + 1;
      }
    }
    return first;
  }

  final SolutionCostPair evaluateAndPackageSolution(T complete) {
    if (pOptInt != null) {
      int cost = pOptInt.cost(complete);
      // update tracker
      boolean isMinCost = pOptInt.isMinCost(cost);
      if (cost < tracker.getCost()) {
        tracker.update(cost, complete, isMinCost);
      }
      return new SolutionCostPair(complete, cost, isMinCost);
    } else {
      double cost = pOpt.cost(complete);
      // update tracker
      boolean isMinCost = pOpt.isMinCost(cost);
      if (cost < tracker.getCostDouble()) {
        tracker.update(cost, complete, isMinCost);
      }
      return new SolutionCostPair(complete, cost, isMinCost);
    }
  }

  abstract SolutionCostPair sample();
}