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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 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 generates solutions to optimization problems using a constructive heuristic. Unless
 * the heuristic given to it is randomized, this class is completely deterministic and has no
 * randomized behavior. Thus, executing the {@link #optimize} method multiple times should produce
 * the same result each time. When using a constructive heuristic, you begin with an empty solution,
 * (e.g., an empty permutation for a permutation optimization problem), and you then use a
 * constructive heuristic to choose which element to add to the partial solution. This is repeated
 * until you derive a complete solution.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public class HeuristicSolutionGenerator> implements SimpleMetaheuristic {

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

  /**
   * Constructs an HeuristicSolutionGenerator for generating solutions to an optimization problem
   * using a constructive heuristic. A ProgressTracker is created for you.
   *
   * @param heuristic The constructive heuristic.
   * @throws NullPointerException if heuristic is null
   */
  public HeuristicSolutionGenerator(ConstructiveHeuristic heuristic) {
    this(heuristic, new ProgressTracker());
  }

  /**
   * Constructs an HeuristicSolutionGenerator for generating solutions to an optimization problem
   * using a constructive heuristic.
   *
   * @param heuristic The constructive heuristic.
   * @param tracker A ProgressTracker
   * @throws NullPointerException if heuristic or tracker is null
   */
  public HeuristicSolutionGenerator(
      ConstructiveHeuristic heuristic, ProgressTracker tracker) {
    if (heuristic == null || tracker == null) {
      throw new NullPointerException();
    }
    this.tracker = tracker;
    this.heuristic = heuristic;
    // default: numGenerated = 0;
    Problem problem = heuristic.getProblem();
    if (heuristic.getProblem() instanceof IntegerCostOptimizationProblem) {
      pOptInt = (IntegerCostOptimizationProblem) problem;
      pOpt = null;
    } else {
      pOpt = (OptimizationProblem) problem;
      pOptInt = null;
    }
  }

  /*
   * package-private for use by split method, and subclasses in same package
   */
  HeuristicSolutionGenerator(HeuristicSolutionGenerator other) {
    // these are threadsafe, so just copy references
    pOpt = other.pOpt;
    pOptInt = other.pOptInt;
    heuristic = other.heuristic;

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

    // default: numGenerated = 0;
  }

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

  @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 HeuristicSolutionGenerator split() {
    return new HeuristicSolutionGenerator(this);
  }

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

  private SolutionCostPair generate() {
    IncrementalEvaluation incEval = heuristic.createIncrementalEvaluation();
    int n = heuristic.completeLength();
    Partial p = heuristic.createPartial(n);
    while (!p.isComplete()) {
      int k = p.numExtensions();
      if (k == 1) {
        if (incEval != null) {
          incEval.extend(p, p.getExtension(0));
        }
        p.extend(0);
      } else {
        double bestH = Double.NEGATIVE_INFINITY;
        int which = 0;
        for (int i = 0; i < k; i++) {
          double h = heuristic.h(p, p.getExtension(i), incEval);
          if (h > bestH) {
            bestH = h;
            which = i;
          }
        }
        if (incEval != null) {
          incEval.extend(p, p.getExtension(which));
        }
        p.extend(which);
      }
    }
    T complete = p.toComplete();
    return evaluateAndPackageSolution(complete);
  }
}