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/*
 * CloudSim Plus: A modern, highly-extensible and easier-to-use Framework for
 * Modeling and Simulation of Cloud Computing Infrastructures and Services.
 * http://cloudsimplus.org
 *
 *     Copyright (C) 2015-2018 Universidade da Beira Interior (UBI, Portugal) and
 *     the Instituto Federal de Educação Ciência e Tecnologia do Tocantins (IFTO, Brazil).
 *
 *     This file is part of CloudSim Plus.
 *
 *     CloudSim Plus 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.
 *
 *     CloudSim Plus 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 CloudSim Plus. If not, see .
 */
package org.cloudsimplus.heuristics;

import org.cloudbus.cloudsim.distributions.ContinuousDistribution;

/**
 * A base class for implementation of
 * Simulated Annealing
 * algorithms used to find a suboptimal solution for a problem defined by sub-classes of this one.
 *
 * The Simulated Annealing is a heuristic that starts with a random solution
 * and iteratively generates a random neighbor solution that its fitness
 * is assessed in order to reach a sub-optimal result.
 * The algorithm try to avoid local maximums, randomly selecting
 * worse solutions to get away from being stuck in these locals.
 *
 * 

The algorithm basically works as follows: *

    *
  1. Starts generating a random solution as you wish;
  2. *
  3. Computes its fitness using some function (defined by the developer implementing the heuristic);
  4. *
  5. Generates a neighbor random solution from the current solution and computes its fitness;
  6. *
  7. Assesses the neighbor and current solution (the conditions below are ensured by the {@link #getAcceptanceProbability()} method): *
      *
    • {@code if neighbor.getFitness() > current.getFitness()} then move to the new solution;
    • *
    • {@code if neighbor.getFitness() < current.getFitness()} then randomly decide if move to the new solution;
    • *
    *
  8. *
  9. Repeat steps 3 to 4 until an aceptable solution is found or some number * of iterations or time is reached. These conditions are defined by the developer * implementing the heuristic.
  10. *
*

* * @param the class of solutions the heuristic will deal with, starting with a random solution * and execute the solution search in order to achieve a satisfying solution (defined by a stop criteria) * @author Manoel Campos da Silva Filho * @see [1] R. A. Rutenbar, * “Simulated Annealing Algorithms: An overview,” IEEE Circuits Devices Mag., vol. 1, no. 5, * pp. 19–26, 1989. * @since CloudSim Plus 1.0 */ public abstract class SimulatedAnnealing> extends HeuristicAbstract { /** * @see #getColdTemperature() */ private double coldTemperature; /** * @see #getCurrentTemperature() */ private double currentTemperature; /** * @see #getCoolingRate() */ private double coolingRate; /** * Instantiates a simulated annealing heuristic. * * @param random a pseudo random number generator * @param solutionClass reference to the generic class that will be used to instantiate heuristic solutions */ SimulatedAnnealing(final ContinuousDistribution random, final Class solutionClass){ super(random, solutionClass); } /** * {@inheritDoc} *

It is used the Boltzmann distribution to define the probability * of a worse solution (considering its cost) * to be accepted or not in order to avoid local minima. * The computed Boltzmann factor also ensures that better solutions are always accepted. * * The Boltzmann Constant has different values depending of the used unit. * In this case, it was used the natural unit of information.

* * @return {@inheritDoc} * * @see Boltzmann distribution * @see Boltzmann constant */ @Override public double getAcceptanceProbability() { final double boltzmannConstant = 1.0; return Math.exp((getBestSolutionSoFar().getCost() - getNeighborSolution().getCost()) / (boltzmannConstant * currentTemperature)); } /** * {@inheritDoc} * * @return true if the system is cold enough and solution search can be * stopped, false otherwise */ @Override public boolean isToStopSearch() { return currentTemperature <= coldTemperature; } /** * {@inheritDoc} * * Cools the system at a the defined {@link #getCoolingRate() cooling rate}. * @see #getCurrentTemperature() () */ @Override public void updateSystemState() { currentTemperature *= 1 - coolingRate; } /** * Gets the current system temperature that * represents the system state at the time * of the method call. * * @return the current system temperature */ public double getCurrentTemperature() { return currentTemperature; } /** * Sets the current system temperature. * @param currentTemperature the temperature to set */ protected void setCurrentTemperature(final double currentTemperature) { this.currentTemperature = currentTemperature; } /** * * @return percentage rate in which the system will be cooled, in scale from [0 to 1[. */ public double getCoolingRate() { return coolingRate; } /** * Sets the percentage rate in which the system will be cooled, in scale from [0 to 1[. * @param coolingRate the rate to set */ public void setCoolingRate(final double coolingRate) { this.coolingRate = coolingRate; } /** * * @return the temperature that defines the system is cold enough * and solution search may be stopped. */ public double getColdTemperature() { return coldTemperature; } /** * Sets the temperature that defines the system is cold enough * and solution search may be stopped. * * @param coldTemperature the cold temperature to set */ public void setColdTemperature(final double coldTemperature) { this.coldTemperature = coldTemperature; } }