/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.examples.nonlinear.tsp.anneal;
import org.encog.examples.nonlinear.tsp.City;
/**
* SolveTSP with Simulated Annealing. The Encog API includes a generic
* simulated annealing problem solver. This example shows how to use it
* to find a solution to the Traveling Salesman Problem (TSP). This
* example does not use any sort of neural network.
* @author
*
*/
public class SolveTSP {
public static final double START_TEMP = 10.0;
public static final double STOP_TEMP = 2.0;
public static final int CYCLES = 10;
public static final int CITIES = 50;
public static final int MAP_SIZE = 256;
public static final int MAX_SAME_SOLUTION = 50;
private TSPSimulatedAnnealing anneal;
private City cities[];
/**
* Place the cities in random locations.
*/
private void initCities() {
cities = new City[CITIES];
for (int i = 0; i < cities.length; i++) {
int xPos = (int) (Math.random() * MAP_SIZE);
int yPos = (int) (Math.random() * MAP_SIZE);
cities[i] = new City(xPos, yPos);
}
}
/**
* Create an initial path of cities.
*/
private void initPath() {
final boolean taken[] = new boolean[this.cities.length];
final Integer path[] = new Integer[this.cities.length];
for (int i = 0; i < path.length; i++) {
taken[i] = false;
}
for (int i = 0; i < path.length - 1; i++) {
int icandidate;
do {
icandidate = (int) (Math.random() * path.length);
} while (taken[icandidate]);
path[i] = icandidate;
taken[icandidate] = true;
if (i == path.length - 2) {
icandidate = 0;
while (taken[icandidate]) {
icandidate++;
}
path[i + 1] = icandidate;
}
}
this.anneal.putArray(path);
}
/**
* Display the cities in the final path.
*/
public void displaySolution() {
Integer path[] = anneal.getArray();
for (int i = 0; i < path.length; i++) {
if (i != 0) {
System.out.print(">");
}
System.out.print("" + path[i]);
}
System.out.println("");
}
/**
* Setup and solve the TSP.
*/
public void solve() {
StringBuilder builder = new StringBuilder();
initCities();
anneal = new TSPSimulatedAnnealing(cities, START_TEMP, STOP_TEMP,
CYCLES);
initPath();
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = Double.MAX_VALUE;
while (sameSolutionCount < MAX_SAME_SOLUTION) {
anneal.iteration();
double thisSolution = anneal.getScore();
builder.setLength(0);
builder.append("Iteration: ");
builder.append(iteration++);
builder.append(", Best Path Length = ");
builder.append(thisSolution);
System.out.println(builder.toString());
if (Math.abs(lastSolution - thisSolution) < 1.0) {
sameSolutionCount++;
} else {
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
System.out.println("Good solution found:");
displaySolution();
}
/**
* Program entry point.
* @param args Not used.
*/
public static void main(String args[]) {
SolveTSP solve = new SolveTSP();
solve.solve();
}
}