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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.operators.permutations;

import java.util.concurrent.ThreadLocalRandom;
import org.cicirello.math.rand.RandomIndexer;
import org.cicirello.math.rand.RandomSampler;
import org.cicirello.permutations.Permutation;
import org.cicirello.search.operators.UndoableMutationOperator;

/**
 * This class implements the Cycle(α) form of cycle mutation on permutations, where one
 * mutation generates a random permutation cycle. Given the original parent permutation and its
 * mutant, a permutation cycle can be defined as follows. Imagine a graph with n vertexes, where n
 * is the permutation length. Now consider that for each index i, we define an edge in that graph
 * between vertex parent[i] and vertex mutant[i]. A permutation cycle consists of all of the
 * elements from one of the cycles in that graph. The length of a cycle is the number of elements in
 * it. Consider an example permutation, p1 = [0, 1, 2, 3, 4], and another permutation, p2 = [0, 3,
 * 2, 1, 4]. This pair of permutations has a 2-cycle (i.e., a cycle of length 2) consisting of
 * elements 1 and 3. Consider a second example, p1 = [0, 1, 2, 3, 4], and p2 = [0, 4, 2, 1, 3]. This
 * example has a 3-cycle consisting of elements 1, 3, and 4. Notice that position 1 has elements 1
 * and 4, position 4 has elements 4 and 3, and position 3 has elements 3 and 1, so in the
 * hypothetical graph described above, there would be edges from 1 to 4, 4 to 3, and 3 to 1, a cycle
 * of length 3.
 *
 * 

The Cycle(α) version of cycle mutation chooses the cycle size randomly from {2, 3, ..., * n} where cycle length k is chosen with probability proportional to αk-2. It then * generates a random permutation cycle of length k. The combination of k elements is chosen * uniformly at random from all possible combinations of k elements. Note that a 2-cycle is simply a * swap. * *

The worst case runtime of a single call to the {@link #mutate(Permutation) mutate} method is * O(n), which occurs when the randomly chosen cycle length is n. However, this is a very low * probability event. Lower cycle lengths are given significantly higher probability. The average * case runtime of a single call to the {@link #mutate(Permutation) mutate} method is O(min(n, * ((2-α)/(1-α))2)). Thus, provided α is not close to 1, the average * runtime is a constant depending upon the value of α. * *

Cycle mutation in both of its forms, including Cycle(α), was introduced in the following * article: * *

Vincent A. Cicirello. 2022. Cycle Mutation: Evolving * Permutations via Cycle Induction, Applied Sciences, 12(11), Article 5506 (June 2022). * doi:10.3390/app12115506 * * @author Vincent A. Cicirello, https://www.cicirello.org/ */ public final class CycleAlphaMutation implements UndoableMutationOperator { private int[] indexes; private final double logAlpha; private final double alpha; // only recompute if permutation length different than last call to mutate private int lastN; private double term; /** * Constructs an CycleAlphaMutation mutation operator. * * @param alpha The alpha parameter of the mutation operator (see class documentation). * @throws IllegalArgumentException if alpha is less than or equal to 0 or greater than or equal * to 1. */ public CycleAlphaMutation(double alpha) { if (alpha <= 0 || alpha >= 1) throw new IllegalArgumentException("alpha is outside allowed range"); logAlpha = Math.log(alpha); this.alpha = alpha; } @Override public final void mutate(Permutation c) { if (c.length() >= 2) { indexes = RandomSampler.sample( c.length(), computeK(c.length(), ThreadLocalRandom.current().nextDouble()), (int[]) null); if (indexes.length > 2) { // randomize order of indexes if there are more than 2 of them // (no need to randomize order if only 2 indexes) for (int j = indexes.length - 1; j > 0; j--) { int i = RandomIndexer.nextInt(j + 1); if (i != j) { int temp = indexes[i]; indexes[i] = indexes[j]; indexes[j] = temp; } } } c.cycle(indexes); } } @Override public final void undo(Permutation c) { if (c.length() >= 2) { if (indexes.length > 2) { for (int i = 0, j = indexes.length - 1; i < j; i++, j--) { int temp = indexes[i]; indexes[i] = indexes[j]; indexes[j] = temp; } } c.cycle(indexes); } } @Override public CycleAlphaMutation split() { return new CycleAlphaMutation(alpha); } /* * package access to support testing */ final int computeK(int n, double u) { if (n != lastN) { term = 1 - Math.pow(alpha, n - 1); lastN = n; } int k = (int) (Math.log(1 - u * term) / logAlpha) + 2; // things get numerically funny in the logs when alpha very near 1 and u also very near 1 // the check in the return protects against this return k > n ? n : k; } }





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