From b6753b5b24710f1a0de26e21548bebf809789296 Mon Sep 17 00:00:00 2001
From: arzba <arzba>
Date: Tue, 12 Nov 2024 16:33:18 +0100
Subject: [PATCH] serie2
---
.vscode/launch.json | 2 +-
README.md | 6 +-
bin/Algorithms.class | Bin 8613 -> 12179 bytes
bin/Ant.class | Bin 0 -> 4271 bytes
bin/Node.class | Bin 1298 -> 1306 bytes
bin/Solution.class | Bin 2953 -> 4529 bytes
bin/cvrp_ls.class | Bin 3268 -> 4206 bytes
src/Algorithms.class | Bin 5088 -> 9489 bytes
src/Algorithms.java | 267 +++++++++++++++++++++++++++++++++++--------
src/Ant.class | Bin 0 -> 3271 bytes
src/Ant.java | 140 +++++++++++++++++++++++
src/Node.java | 2 +-
src/Solution.class | Bin 2155 -> 3634 bytes
src/Solution.java | 53 ++++++++-
src/cvrp_ls.class | Bin 2593 -> 3850 bytes
src/cvrp_ls.java | 32 ++++--
16 files changed, 438 insertions(+), 64 deletions(-)
create mode 100644 bin/Ant.class
create mode 100644 src/Ant.class
create mode 100644 src/Ant.java
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 19b9e6e..2d3f6be 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -16,7 +16,7 @@
"request": "launch",
"mainClass": "cvrp_ls",
"projectName": "optialgos_92db6011",
- "args": ["C:\\Users\\Jule Behrens\\Documents\\JonahAlgos\\optialgos\\src\\instances\\Loggi-n401-k23.vrp", "taboo_search", "3600", "50"]
+ "args": ["C:\\Users\\Jule Behrens\\Documents\\JonahAlgos\\optialgos\\src\\instances\\Loggi-n401-k23.vrp", "taboo_search", "14400"]
}
]
}
\ No newline at end of file
diff --git a/README.md b/README.md
index c78d38a..91abf4d 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,8 @@ zeigen muss.
Das Programm gibt die Kosten für die Initial ermittelte Lösung mit einem Greedy-Approach und die mit basic local search ohne Zeitlimit optimierte Lösung und anschließend die Lösungen selbst im .sol Format aus.
Alternativ kann ein Algorithmus spezifiziert werden mit "java cvrp_ls *absolutePath* *algorithm*". Dabei sind die Optionen für *algorithm* "basic_local_serach", "taboo_search" und "ant_colony".
-Für "taboo_search" und "ant_colony" muss zudem eine maximale Laufzeit für die Optimierung in vollen Sekunden angegeben werden ("java cvrp_ls *absolutePath* *algorithm* *maxTimeInSeconds*"). Für "basic_local_serach" ist das optional.
+Für "taboo_search" und "ant_colony" muss zudem eine maximale Laufzeit (integer) für die Optimierung in vollen Sekunden angegeben werden ("java cvrp_ls *absolutePath* *algorithm* *maxTimeInSeconds*"). Für "basic_local_search" ist das optional.
-"taboo_search" kann optional ein weiterer Parameter mitgegeben werden: Die Anzahl der Taboos. Der Standardwert liegt bei *Anzahl der Knoten/3* abgerundet. Ein möglicher Aufruf wäre: "java cvrp_ls *absolutePath* taboo_search *maxTimeInSeconds* *numberOfTaboos*".
\ No newline at end of file
+"taboo_search" kann optional ein weiterer Parameter mitgegeben werden: Die Anzahl der Taboos (integer). Der Standardwert liegt bei *Anzahl der Knoten/2* abgerundet. Ein möglicher Aufruf wäre: "java cvrp_ls *absolutePath* taboo_search *maxTimeInSeconds* *numberOfTaboos*".
+
+"ant_colony" können optional weitere Parameter mitgegeben werden: Die Anzahl der Ameisen (Standardwert: *Anzahl der Knoten x 5*), Anzahl der Pheromone, die eine Ameise zu einer Kante addiert (Standardwert: 10, integer), Anzahl der Pheromone, die in einer minor round verloren gehen (Standardwert: 1, integer), ob die Pheromone zwischen major rounds zurückgesetzt werden sollen (Standard: 0 (nein), integer[0 = nein, alles andere = ja]) und die Strategie zur Auswahl des ersten Knotens der Ameisen ("depot" oder "even", mit Standard "even"). Dabei bedeutet "depot", dass alle Ameisen vom Depot aus starten, während "even" bedeutet, dass die Ameisen gleichmäßig verteilt auf den Knoten starten, wobei ein erster Weg vom Depot aus impliziert ist. Ein möglicher Aufruf wäre: "java cvrp_ls *absolutePath* ant_colony *maxTimeInSeconds* *numberOfAnts* *numberOfPheromonesAdded* *numberOfPheromonesDecayes* *resetPheromonesBetweenMajorRounds* *antDeployStrategy*".
\ No newline at end of file
diff --git a/bin/Algorithms.class b/bin/Algorithms.class
index b6ae11b222029b818ae7893b44664a06c8973154..35d59ae45df963d6188d6319629084a5f37e3b36 100644
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diff --git a/bin/Ant.class b/bin/Ant.class
new file mode 100644
index 0000000000000000000000000000000000000000..3de4d524734dece42485a07e8a9cb06237c50480
GIT binary patch
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diff --git a/bin/Node.class b/bin/Node.class
index 05e69ee717aba2ef28568fa6305e7e5f8f7250fa..42144557dfcd10696d3f49782dc4f89c50bb3dbe 100644
GIT binary patch
delta 83
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delta 47
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diff --git a/bin/Solution.class b/bin/Solution.class
index 2cf9957a6dd60cf27a03de8ecc06e93c2f4377c3..89ae1e30c6e273bfdc0d9a6b53c5c1867ac05895 100644
GIT binary patch
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diff --git a/bin/cvrp_ls.class b/bin/cvrp_ls.class
index b881a47e3112097351bffd1753ab79d4097b2e25..0be1a074a6884820ab71538bbe23f451418625d5 100644
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diff --git a/src/Algorithms.class b/src/Algorithms.class
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GIT binary patch
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diff --git a/src/Algorithms.java b/src/Algorithms.java
index 590b7d1..37db335 100644
--- a/src/Algorithms.java
+++ b/src/Algorithms.java
@@ -3,8 +3,13 @@ import java.util.Arrays;
public class Algorithms {
+ int pheromonesStrength = -1;
+ String antStartStrategy = "";
+ int pheromonesDecayStrength = -1;
String algorithmType;
+ boolean resetPheromones = false;
int maxRuntimeInSeconds = -1;
+ int numberOfAnts = -1;
int numberOfTaboos = -1;
public Algorithms(){}
public Algorithms(String algorithmType, String[] optionalParams) throws Exception{
@@ -20,8 +25,29 @@ public class Algorithms {
this.algorithmType = "basic_local_search";
break;
case "ant_colony":
+ try {
+ this.numberOfAnts = Integer.valueOf(optionalParams[1]);
+ this.pheromonesStrength = Integer.valueOf(optionalParams[2]);
+ this.pheromonesDecayStrength = Integer.valueOf(optionalParams[3]);
+ if(Integer.valueOf(optionalParams[4]) == 0){
+ this.resetPheromones = false;
+ } else {
+ this.resetPheromones = true;
+ }
+ this.antStartStrategy = optionalParams[4];
+ } catch (Exception e) {
+
+ }
if(maxRuntimeInSeconds <= 0)
throw new Exception();
+ if(this.pheromonesStrength <= 0)
+ System.out.println("Using standard value for pheromone strength.");
+ if(this.antStartStrategy.equals(""))
+ System.out.println("Using standard value for ant start strategy.");
+ if(this.pheromonesDecayStrength <= 0)
+ System.out.println("Using standard value for pheromone decay strength.");
+ if(this.numberOfAnts <= 0)
+ System.out.println("Using standard value for number of ants.");
break;
case "taboo_search":
try {
@@ -45,6 +71,15 @@ public class Algorithms {
case "greedy":
return generateInitialSolutionGreedy(instance);
case "ant_colony":
+ //setting default parameters if needed
+ if(this.numberOfAnts <= 0)
+ numberOfAnts = Math.multiplyExact(instance.getDimension(),5);
+ if(this.pheromonesDecayStrength <= 0)
+ pheromonesDecayStrength = 1;
+ if(this.pheromonesStrength <= 0)
+ pheromonesStrength = 10;
+ if(this.antStartStrategy.equals(""))
+ antStartStrategy = "even";
return generateInitialSolutionAnt(instance);
default:
return null;
@@ -58,20 +93,21 @@ public class Algorithms {
case "greedy":
return generateInitialSolutionGreedy(solution.instance);
case "taboo_search":
+ //setting default parameters if needed
if(this.numberOfTaboos <= 0)
numberOfTaboos = solution.instance.getDimension()/3;
return tabooSearch(solution);
case "ant_colony":
- return generateInitialSolutionAnt(solution.instance);
+ return generateSolution(solution.instance);
default:
- return null;
+ return localSearch(solution);
}
}
private Solution tabooSearch(Solution solution) {
int[] state = {2,0,0,0};
int newBestCount = 0;
-
+ int time =0;
Solution currentSolution = solution;
Solution bestSolutionSeen = solution;
@@ -86,10 +122,28 @@ public class Algorithms {
while(maxRuntimeInSeconds > 0 && System.currentTimeMillis()<end){
- if(possibleBetterNeighbor.getCost() < currentBestNeighbor.getCost()){
- currentBestNeighbor = possibleBetterNeighbor;
- currentBestNeighborNodeChanged = state[0];
+ if(System.currentTimeMillis()>start+1000 && time == 0){
+ try{System.out.println("1 Sekunde: " + bestSolutionSeen.getCost());}catch(Exception e){}
+ time++;
+ }
+ if(System.currentTimeMillis()>start+10000 && time == 1){
+ try{System.out.println("10 Sekunde: " + bestSolutionSeen.getCost());}catch(Exception e){}
+ time++;
}
+ if(System.currentTimeMillis()>start+60000 && time == 2){
+ try{System.out.println("60 Sekunde: " + bestSolutionSeen.getCost());}catch(Exception e){}
+ time++;
+ }
+ try {
+ if(possibleBetterNeighbor.getCost() < currentBestNeighbor.getCost()){
+ currentBestNeighbor = possibleBetterNeighbor;
+ currentBestNeighborNodeChanged = state[0];
+ }
+ } catch (Exception e) {
+
+
+ }
+
//permutate state to get another neighbor which is not taboo
state[0]++;
while(taboos.contains(state[0])){
@@ -111,17 +165,22 @@ public class Algorithms {
if(state[3] == 2){
currentSolution = currentBestNeighbor;
//System.out.println("Made a step, taboos: "+taboos.size());
- if(currentSolution.getCost() < bestSolutionSeen.getCost()){
- bestSolutionSeen = currentSolution;
-
- System.out.println("Found new best solution "+(++newBestCount) + ": All "+taboos.size()+" taboos dropped");
- taboos.clear();
- } else {
- taboos.add(currentBestNeighborNodeChanged);
- if(taboos.size() > numberOfTaboos){
- taboos.remove(0);
+ try {
+ if(currentSolution.getCost() < bestSolutionSeen.getCost()){
+ bestSolutionSeen = currentSolution;
+
+ System.out.println("Found new best solution "+(++newBestCount) + ": All "+taboos.size()+" taboos dropped");
+ taboos.clear();
+ } else {
+ taboos.add(currentBestNeighborNodeChanged);
+ if(taboos.size() > numberOfTaboos){
+ taboos.remove(0);
+ }
}
+ } catch (Exception e) {
+
}
+
int[] help = {2,0,0,0};
state = help;
currentBestNeighbor = getOtherNeighbor(state, currentSolution);
@@ -136,8 +195,116 @@ public class Algorithms {
}
private Solution generateInitialSolutionAnt(Instance instance) {
- // TODO Auto-generated method stub
- throw new UnsupportedOperationException("Unimplemented method 'generateInitialSolutionAnt'");
+ Solution bestSeenSolution = null;
+ int time = 0;
+ int newBestSolutionCount = 0;
+ int[][] pheromonesOnEdge = new int[instance.getDimension()+1][instance.getDimension()+1];
+ //initialize all edges with phermone score 1
+ for (int i = 0; i < pheromonesOnEdge.length; i++) {
+ for (int j = 0; j < pheromonesOnEdge[i].length; j++) {
+ pheromonesOnEdge[i][j] = 1;
+ }
+ }
+
+ long start = System.currentTimeMillis();
+ long end = start + this.maxRuntimeInSeconds * 1000;
+
+ while(maxRuntimeInSeconds > 0 && System.currentTimeMillis()<end){
+ //if optional parameter is set, reset pheromones inbetween major rounds
+ if(System.currentTimeMillis()>start+1000 && time == 0){
+ try{System.out.println("1 Sekunde: " + bestSeenSolution.getCost());}catch(Exception e){}
+ time++;
+ }
+ if(System.currentTimeMillis()>start+10000 && time == 1){
+ try{System.out.println("10 Sekunde: " + bestSeenSolution.getCost());}catch(Exception e){}
+ time++;
+ }
+ if(System.currentTimeMillis()>start+60000 && time == 2){
+ try{System.out.println("60 Sekunde: " + bestSeenSolution.getCost());}catch(Exception e){}
+ time++;
+ }
+ if(resetPheromones) {
+ for (int i = 0; i < pheromonesOnEdge.length; i++) {
+ for (int j = 0; j < pheromonesOnEdge[i].length; j++) {
+ pheromonesOnEdge[i][j] = 1;
+ }
+ }
+ }
+
+ ArrayList<Ant> ants = new ArrayList<Ant>();
+ for (int i = 0; i < numberOfAnts; i++) {
+ switch (antStartStrategy) {
+ case "depot":
+ ants.add(new Ant(instance));
+ break;
+ case "even":
+ default:
+ ants.add(new Ant(instance, i+1));
+ break;
+ }
+
+ }
+ while(!antsAreAllDone(ants)){
+ ArrayList<int[]> walkedEdges = new ArrayList<int[]>();
+ //let ants do one minor round
+ for (Ant ant : ants) {
+ int[] walkedEdge = ant.makeStepInMinorRound(pheromonesOnEdge);
+ walkedEdges.add(walkedEdge);
+ }
+ //update pheromones after minor round
+ for (int[] walkedEdge : walkedEdges) {
+ if(walkedEdge != null){
+ if(walkedEdge[0] < walkedEdge[1]){
+ pheromonesOnEdge[walkedEdge[1]][walkedEdge[0]] += pheromonesStrength;
+ } else {
+ pheromonesOnEdge[walkedEdge[0]][walkedEdge[1]] += pheromonesStrength;
+ }
+ }
+ }
+ //let pheromones decay (not under 1)
+ for (int i = 0; i < pheromonesOnEdge.length; i++) {
+ for (int j = 0; j < pheromonesOnEdge[i].length; j++) {
+ pheromonesOnEdge[i][j] -= pheromonesDecayStrength;
+ if(pheromonesOnEdge[i][j]<1){
+ pheromonesOnEdge[i][j] = 1;
+ }
+ }
+ }
+ }
+ //check if ant has discovered new best solution
+ for (Ant ant : ants) {
+ Solution antsSolution = ant.toSolution();
+
+ if(bestSeenSolution == null){
+ System.out.println("Found new best solution "+(++newBestSolutionCount));
+ bestSeenSolution = antsSolution;
+ } else {
+ try {
+ if(bestSeenSolution.getCost() > antsSolution.getCost()){
+ System.out.println("Found new best solution "+(++newBestSolutionCount));
+ bestSeenSolution = antsSolution;
+ }
+ } catch (Exception e) {
+
+ }
+ }
+
+
+ }
+ }
+
+ return bestSeenSolution;
+ }
+
+ private boolean antsAreAllDone(ArrayList<Ant> ants){
+ boolean allDone = true;
+ for (Ant ant : ants) {
+ if(!ant.done){
+ allDone = false;
+ break;
+ }
+ }
+ return allDone;
}
//primitive greedy Approach
@@ -200,7 +367,6 @@ public class Algorithms {
i++;
}
solution.tours = convertedTours;
- solution.getCost();
return solution;
}
@@ -209,50 +375,55 @@ public class Algorithms {
Solution currentBestSolution = solution;
int[] state = {2,0,0,0};
Solution possibleBetterSolution = getOtherNeighbor(state, currentBestSolution);
- int newBestCount = 0;
+ int newBestSolutionCount = 0;
long start = System.currentTimeMillis();
long end = start + this.maxRuntimeInSeconds * 1000;
//tries all neighbors as possibleBetterSolution, if better solution found, change currentBestSolution and search from there
//terminates when it cannot find a better solution in its neighbarhood
while (possibleBetterSolution != null) {
-
+
//stop if max execution time has exceeded
if(maxRuntimeInSeconds > 0 && System.currentTimeMillis()>end){
System.out.println("terminated by time limit");
break;
}
- if(possibleBetterSolution.getCost() < currentBestSolution.getCost()){
- currentBestSolution = possibleBetterSolution;
- int[] help = {2,0,0,0};
- state = help;
- possibleBetterSolution = getOtherNeighbor(state, currentBestSolution);
- System.out.println("found new best solution "+(++newBestCount));
- }else{
- //permutate state to get another neighbor
- state[0]++;
- if(state[0] > currentBestSolution.instance.getDimension()){
- //System.out.println("reset extracted node");
- state[0] = 2;
- state[2]++;
- if(state[2] > currentBestSolution.tours[state[1]].length-1){
- //System.out.println("reset position");
- state[2] = 0;
- state[1]++;
- if(state[1] > currentBestSolution.tours.length-1){
- //System.out.println("reset tours");
- state[1] = 0;
- state[3]++;
- //stop if all neighbors are worse/local optimum is reached
- if(state[3] == 2){
- System.out.println("terminated by local optimum");
- break;
+ try {
+ if(possibleBetterSolution.getCost() < currentBestSolution.getCost()){
+ currentBestSolution = possibleBetterSolution;
+ int[] help = {2,0,0,0};
+ state = help;
+ possibleBetterSolution = getOtherNeighbor(state, currentBestSolution);
+ System.out.println("Found new best solution "+(++newBestSolutionCount));
+ }else{
+ //permutate state to get another neighbor
+ state[0]++;
+ if(state[0] > currentBestSolution.instance.getDimension()){
+ //System.out.println("reset extracted node");
+ state[0] = 2;
+ state[2]++;
+ if(state[2] > currentBestSolution.tours[state[1]].length-1){
+ //System.out.println("reset position");
+ state[2] = 0;
+ state[1]++;
+ if(state[1] > currentBestSolution.tours.length-1){
+ //System.out.println("reset tours");
+ state[1] = 0;
+ state[3]++;
+ //stop if all neighbors are worse/local optimum is reached
+ if(state[3] == 2){
+ System.out.println("terminated by local optimum");
+ break;
+ }
}
}
- }
- }
- possibleBetterSolution = getOtherNeighbor(state, currentBestSolution);
+ }
+ possibleBetterSolution = getOtherNeighbor(state, currentBestSolution);
+ }
+ } catch (Exception e) {
+
}
+
}
diff --git a/src/Ant.class b/src/Ant.class
new file mode 100644
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diff --git a/src/Ant.java b/src/Ant.java
new file mode 100644
index 0000000..121e4ff
--- /dev/null
+++ b/src/Ant.java
@@ -0,0 +1,140 @@
+ import java.util.ArrayList;
+import java.util.Arrays;
+
+public class Ant {
+ Instance instance;
+ Node currentNode;
+ ArrayList<Node> currentTour = new ArrayList<Node>();
+ ArrayList<ArrayList<Node>> tours = new ArrayList<ArrayList<Node>>();
+ ArrayList<Node> notVisitedNodes;
+ boolean done = false;
+
+ public Ant(Instance instance){
+ this.instance = instance;
+ notVisitedNodes = new ArrayList<Node>(Arrays.asList(instance.getNodes()));
+ notVisitedNodes.remove(instance.getDepot());
+ currentNode = instance.getDepot();
+ notVisitedNodes.remove(0);
+ }
+
+ public Ant(Instance instance, int startNodeNumber){
+ Node currentNode;
+ currentNode = instance.getDepot();
+ this.instance = instance;
+ notVisitedNodes = new ArrayList<Node>(Arrays.asList(instance.getNodes()));
+ for (Node node : notVisitedNodes) {
+ if(node != null && node.number == (startNodeNumber%instance.getDimension())+1){
+ currentNode = node;
+ break;
+ }
+ }
+ notVisitedNodes.remove(instance.getDepot());
+ notVisitedNodes.remove(0);
+ notVisitedNodes.remove(currentNode);
+ this.currentNode = currentNode;
+ currentTour.add(currentNode);
+
+ }
+
+ public int[] makeStepInMinorRound(int[][] pheromonesOnEdge){
+ //if ant is done with tour return null, else return edge, that she walked
+ if(this.done){
+ return null;
+ }
+ Node nextNode = null;
+
+ double currentProbability = 0;
+ ArrayList<double[]> probabilities = new ArrayList<double[]>();
+ if(currentNode.depot){
+ currentTour = new ArrayList<Node>();
+ } else {
+ double distance;
+ double pheromones;
+ if(currentNode.number < instance.getDepot().number){
+ distance = instance.getEdgeWeights()[instance.getDepot().number][currentNode.number];
+ pheromones = pheromonesOnEdge[instance.getDepot().number][currentNode.number];
+ } else {
+ distance = instance.getEdgeWeights()[currentNode.number][instance.getDepot().number];
+ pheromones = pheromonesOnEdge[instance.getDepot().number][currentNode.number];
+ }
+ double probability = pheromones / distance;
+ double[] probabilityInterval= new double[3];
+ probabilityInterval[0] = currentProbability;
+ probabilityInterval[1] = currentProbability+probability;
+ probabilityInterval[2] = instance.getDepot().number;
+ probabilities.add(probabilityInterval);
+ currentProbability += probability;
+ }
+ for (Node node : notVisitedNodes) {
+ if(node.demand + sumOfDemands(currentTour) <= instance.getTruckCapacity()){
+ double distance;
+ double pheromones;
+ if(currentNode.number < node.number){
+ distance = instance.getEdgeWeights()[node.number][currentNode.number];
+ pheromones = pheromonesOnEdge[node.number][currentNode.number];
+ } else {
+ distance = instance.getEdgeWeights()[currentNode.number][node.number];
+ pheromones = pheromonesOnEdge[node.number][currentNode.number];
+ }
+ double probability = pheromones / (distance+1);
+ double[] probabilityInterval= new double[3];
+ probabilityInterval[0] = currentProbability;
+ probabilityInterval[1] = currentProbability+probability;
+ probabilityInterval[2] = node.number;
+ probabilities.add(probabilityInterval);
+ currentProbability += probability;
+ }
+ }
+ double randomNumber = Math.random()*currentProbability;
+ int nextNodeNumber = -1;
+ for (double[] interval : probabilities) {
+ if(randomNumber >= interval[0] && randomNumber < interval[1]){
+ nextNodeNumber = (int)interval[2];
+ }
+ }
+
+ if(nextNodeNumber == instance.getDepot().number){
+ tours.add(currentTour);
+ nextNode = instance.getDepot();
+ if(notVisitedNodes.size() == 0){
+ this.done = true;
+ }
+ } else {
+ for (Node node : notVisitedNodes) {
+ if(node.number == nextNodeNumber){
+ currentTour.add(node);
+ nextNode = node;
+ notVisitedNodes.remove(node);
+ break;
+ }
+ }
+ }
+
+
+ int[] walkedEdge = {currentNode.number,nextNode.number};
+ currentNode = nextNode;
+
+ return walkedEdge;
+ }
+
+ private int sumOfDemands(ArrayList<Node> currentTour){
+ int demand = 0;
+ for (Node node : currentTour) {
+ demand += node.demand;
+ }
+ return demand;
+ }
+
+ public Solution toSolution(){
+ Solution sol = new Solution(instance);
+ Node[][] tours = new Node[this.tours.size()][];
+ int i = 0;
+ for (ArrayList<Node> tour : this.tours) {
+ tours[i] = new Node[tour.size()];
+ tours[i] = tour.toArray(tours[i]);
+ i++;
+ }
+ sol.tours = tours;
+ return sol;
+ }
+}
diff --git a/src/Node.java b/src/Node.java
index 1f7287b..371ea59 100644
--- a/src/Node.java
+++ b/src/Node.java
@@ -14,6 +14,6 @@ public class Node {
public void setDepot(){this.depot = true;}
public void setDemand(Integer demand){this.demand = demand;}
public String toString(){
- return ""+this.number;
+ return ""+this.number+"("+this.demand+")";
}
}
diff --git a/src/Solution.class b/src/Solution.class
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z23Cel21bTP1_lOW1}3mRey!~cj9ScF8Q8V7w=(c<WME)mWN2c5WEchp9tIW$E(T5p
zUIsn}J_bfmsmj0vGJx?I0}IGlM#0?-f|1)9gnhKOFo^i-?6YGwV-aPMUBs|YN0i0L
zij~=nO=}AS?=}VrD|Sg%D-KDHeP*1@W?Z6NqMVBu5+pf9xs0s1CAp6=_-RXWZ)1=P
zmt^0<pd`t?g+XZvgPbT-w+2|$2rR6ZV8*tbK^h?iQfmZO0FsjAwqkGLVqE@zK0*b^
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z&|zd?&}9UbPt6Pr4DJkE{~3fCSlJmk*ck-b8Cn<_85sUEC^K*)iLf(dF|so#gD6G@
zhCd9B><r4{zZjT*FtGk$F#5s3DlN&-3QjzX3<eC0pyCi3yNL`e4BQM13`exKF}R0=
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zI3-y@vB?RJO(m_!Ees%oc(*ZVfF+D1K{9#?TN%8UGsvSzgCvZ=ionv6oK~#M8Cb2j
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zz<?6)Z480XeEE@qnSqOefuR%<w0<BDXl-F&-^CEbz<`m*JRx~Zdly3(1A~twD|#fz
zKqEmL9L&2IA{iKbz~T2;dly3thyzOQ+qH#ah2pm{ByNBwT{Q+y1_lN%22loY1|0?;
z24e<a244n0hDZiqhIobmhBO9mh8%_<a7frOu>5CGfF{Kxa3Fw^A}mZGBH&=j0wvNP
z4D$aOco>+#=`;yk-n27xK)pYOffZbodGBW6hy(?}c7}8xZBQU=1t%PleR~<Qe3|WJ
zL8)=yA_jRKFiRE``y#SN`*two9AL-_21ouqZJn(QBH9Q~DS^X1oPmcSf<c5Kl0lXs
zia~=Rn!%1Ciot~;mcf%D4(uU*2Bv=u@?iJ1F#cjtVPItFghddeAOkblm|~$KA7)z_
zs5^YLWmpn+F_b{#NXE!dcb^#xvl*)>t0>DN1_fPFRwFAmZArE*3`)BgN*Ne@Kxvu1
zg_Uvn|NGjz7|K9#!Nj=y|3+=CZ4BkR7^=WZ_<eP@F;r{sVyI(a@X_ADz{Ft8V9(&h
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zk{F5^QW**v(in;u3K>elzP4cC_zQ}}B*uRX3g9B+KZ7&_J2)7U7{LPU49cL?3r)+v
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TDFXuo7Xu^1GKLils~98!lUy}N
delta 1339
zcmdla^ICxG)W2Q(7#J8F88Rkvd4*)=6_+IDC8shnaQS#b+18v491MIs4EziNj0`Nv
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zKd=a^2{AA+XbUkgGw5?Lm@qOh>BMp{m@zUi#&R)OFj%rPSn)7eGuTX?$(&(e$HTzN
zV9&_FtAX&iW*9qzBO?Pl+*i&FF6<1hJPd9O?vo8!JZu;lSe)}yQW+WeeKPY>{lLBn
zNleN~Wn^GaPc3l<hZVDirY9o<Z*WOsa<*?`0a%`$L1eNGtMcS@Rs*+CMh5Y`#N1RX
z1x7AV=z{1FP=Hz~a4>{3GB7i8MRPDjf*6cC91PKn3`~r%{A>)dTnupx@$3u<Aa5s5
z)@8fQm@=7>U5hbovJSg(Ju?FX12+Q?0|*E+h%hiPFoB|wfq{XAff3|a21W)C1_lN#
z1||k31_lN$t?dkqTH0F~*f%mTFfcNBGB7akF@Q|rWME<7V&G)pW?*FSV(^COV~k*6
zVc=$9VA!w4yp@4hYYT&*kM=%WW;<D#MGPyoWx0&3ShTh<uy11!oqUH=p<Y^&a|?s?
z5(Xu$$Sq*y?AsU=Bw4pGNE?A=^b)o*@GfVNN0A0;F#;<BOG|QEu`Xv|wc={wU|jxx
z9*R=1Nl**7Fi3;dw=gp<{~w{Xg@JcXJp&_y4MPV*H-icT6WDK7418c8vNG^9NHPd8
zC@}~!*f0n)cru7EWH5*^v@l38bTG&;bTddY^fCxCOlFX0*uuciu!}*F!H0o?!JdKR
z9|J!-gD)fFe+D@QPId+kFo&JNft^8yoxy>Tk%8e4D3!@byRnBbsQhAJ{K3HbpFxd*
zk)4499L9bOnvl?Bc+0@dz{SA8PzdpY8pzSR7_=A|d_W=VtFzCJ*^EV$MRpN`r;aF#
zkrk`<E(X2H3EWEc(z_T885n%PLGoC87lRRq!@{`y|8{L5V<FRR4CWiaaSw`5H3m)w
z1_l)dK?YR@4F)v^eFk*~PX-N!PzH5|Xa+5YBnDN63<e#r|Lho8{xirhu!8+%0rnaL
z1Hz{e5q5@p2dGznFv$OB;9+2bs0Vw`pCJJ1y(tW=3@i)`4BopLI3ht_+|FR@qYd&T
zH0|u$%i!S4Y$pry?Y>0}@;YFatPzMWYqW0%gVO;9hhT7o-qY6E${?r>^;IbY1A`Jc
zI1L$i7>pQ17>pTY8B7>77)%-L7)%&k7|a<w87#nF;%8v`#~=@OpfBSu1{DTohCp!G
q1Th3NFfd3lurS0hFfxRI$uKY(0Vbmul0Z3xfsrAZA(bJWK@tGwDC#}{
diff --git a/src/Solution.java b/src/Solution.java
index 9d53c54..afde669 100644
--- a/src/Solution.java
+++ b/src/Solution.java
@@ -1,16 +1,57 @@
import java.util.Arrays;
+import java.util.ArrayList;
+import java.util.HashSet;
//represents a feasible solution to a instance
public class Solution {
Instance instance;
Node[][] tours;
private int cost;
+ private boolean feasible = false;
public Solution(Instance instance){
this.instance = instance;
}
- public int getCost(){
+ public boolean isFeasible() throws Exception{
+ ArrayList<Node> allVisitedNodes = new ArrayList<Node>();
+ for (Node[] tour : this.tours) {
+ int tourCost = 0;
+ int tourDemand = 0;
+ Node lastNode = instance.getDepot();
+ for (Node node : tour) {
+ if(node != null){
+ tourDemand += node.demand;
+ allVisitedNodes.add(node);
+ if(lastNode.number > node.number){
+ tourCost += instance.getEdgeWeights()[lastNode.number][node.number];
+ } else {
+ tourCost += instance.getEdgeWeights()[node.number][lastNode.number];
+ }
+ lastNode = node;
+ }
+ }
+ tourCost += instance.getEdgeWeights()[lastNode.number][instance.getDepot().number];
+ //check if tour is in capacity limit
+ if(tourDemand > instance.getTruckCapacity()) {
+ throw new Exception("capacity problem ("+tourDemand + " greater " +instance.getTruckCapacity()+")");
+ }
+
+ }
+ //check if nodes are duplicates
+ if(allVisitedNodes.size() != new HashSet<Node>(allVisitedNodes).size()) {
+ throw new Exception("duplicate problem");
+ }
+ //check if right number of nodes is in all tours
+ if(allVisitedNodes.size() != instance.getDimension()-1) {
+ throw new Exception("not all nodes problem");
+ }
+
+ return this.feasible = true;
+ }
+
+ public int getCost() throws Exception{
+ isFeasible();
this.cost = 0;
for (Node[] tour : tours) {
Node lastNode = instance.getDepot();
@@ -32,7 +73,7 @@ public class Solution {
return this.cost;
};
public String toString(){
- String s = "name: "+instance.getName()+ "\n" + "cost: " + cost + "\nTours: ";
+ String s = "name: "+instance.getName()+ "\n" + "cost: " + cost + "feasible: " + feasible + "\nTours: ";
for (Node[] tour : tours) {
s += "\n[";
for (Node node : tour) {
@@ -57,6 +98,7 @@ public class Solution {
public String toSol(){
String rep = "";
+
for (int i = 0; i < tours.length; i++) {
rep += "Route #"+(i+1)+":";
for (Node node : tours[i]) {
@@ -68,7 +110,12 @@ public class Solution {
}
rep += "\n";
}
- rep += "Cost " + this.getCost();
+ try {
+ rep += "Cost " + this.getCost();
+ } catch (Exception e) {
+ rep = "not feasible: " + e.getMessage() + "\n" + rep;
+ }
+
return rep;
}
}
\ No newline at end of file
diff --git a/src/cvrp_ls.class b/src/cvrp_ls.class
index 98308e98a6a7e534721ee16364b6e19e9714c6e5..5a06464d68874cdd6103905a145b635ac59db0a5 100644
GIT binary patch
literal 3850
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X$*l}~85kJ27#JD$F&tnx#2^U(@FVyY
delta 1430
zcmeB@TPVVH>ff$?3=9m84BZpC>@&C+co=xu8Tfb@_!$Hk83eKt%M$f-67$magDZ<m
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zSRo}pFBK%~0h0A(WDwKv3C_<c1$ow56T#$UuwwA$Venz_oy^6o$|c0Wz~Ilr5Wo;P
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zG@A%BJ45E=scfOT1&j>B(LRXS3N9(i%uBasXDC8VPGI55jO=zS6^sl?lRG(7n5r@-
zujjC1teX6eBZskOax|x+S{);UAftks0t_oCD8$6@voSPqF*GtXu`@J-+|e@mFz02K
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zhR|IMNnm4^GsvQv0MfCGAr&mSk%5UprJliz!ID9m!G|G?A(BCwfe9S8RSa^V0B2xk
zU}IooU}xZ9;AG%s;9`(q;AT)^5Ma<?5M<C{5MnT65M!`p5N5Du5Mgj;kYVs)kYfmA
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diff --git a/src/cvrp_ls.java b/src/cvrp_ls.java
index 481c199..df0ca02 100644
--- a/src/cvrp_ls.java
+++ b/src/cvrp_ls.java
@@ -42,15 +42,29 @@ public class cvrp_ls {
}
Solution optimizedSolution = chosen.generateSolution(greedySolution);
-
+
//print results
- System.out.println(("\nAfter initial greedy Approach, optimized with "+chosen.algorithmType+" with a maximum runtime of approximately "+chosen.maxRuntimeInSeconds+" seconds!").replace("-1", "unlimited"));
- System.out.println("Instance name"+" & "+"Cost initial Solution"+" & "+"Cost optimized solution ("+chosen.algorithmType+")"+" & " + "Cost optimal solution (100%)"+ " \\\\");
- if(instance.getOptimalCost() > 0)
- System.out.println(instance.getName()+" & "+greedySolution.getCost()+" ("+(float)greedySolution.getCost()/(float)instance.getOptimalCost()*100+"%) & "+optimizedSolution.getCost()+" ("+(float)optimizedSolution.getCost()/(float)instance.getOptimalCost()*100+"%) & "+instance.getOptimalCost() + " \\\\");
- else
- System.out.println(instance.getName()+" & "+greedySolution.getCost()+" & "+optimizedSolution.getCost()+" & "+ " \\\\");
- System.out.println("\n Initial solution:\n"+greedySolution.toSol());
- System.out.println("\n Optimized solution:\n"+optimizedSolution.toSol());
+ if(chosen.algorithmType.equals( "ant_colony") || chosen.algorithmType.equals("greedy")){
+ System.out.println(("\nSolved with "+chosen.algorithmType+" with a maximum runtime of approximately "+chosen.maxRuntimeInSeconds+" seconds!").replace("-1", "unlimited"));
+
+ if(chosen.algorithmType.equals("ant_colony"))
+ System.out.println("Ants: "+chosen.numberOfAnts+", Phermones Added: "+chosen.pheromonesStrength+", Pheromones Decayed: "+ chosen.pheromonesDecayStrength + ", Deploy Strategy: "+chosen.antStartStrategy+", Reset after Major round: "+chosen.resetPheromones);
+ System.out.println("Instance name"+" & "+" "+" & "+"Cost solution ("+chosen.algorithmType+")"+" & " + "Cost upper bound (100%)"+ " \\\\");
+ if(instance.getOptimalCost() > 0)
+ System.out.println(instance.getName()+" & " + " & "+optimizedSolution.getCost()+" ("+(float)optimizedSolution.getCost()/(float)instance.getOptimalCost()*100+"%) & "+instance.getOptimalCost() + " \\\\");
+ else
+ System.out.println(instance.getName()+" & "+greedySolution.getCost()+" & "+optimizedSolution.getCost()+" & "+ " \\\\");
+ System.out.println("\n Solution:\n"+optimizedSolution.toSol());
+ } else {
+ System.out.println(("\nAfter initial greedy Approach, optimized with "+chosen.algorithmType+" with a maximum runtime of approximately "+chosen.maxRuntimeInSeconds+" seconds!").replace("-1", "unlimited"));
+ System.out.println("Instance name"+" & "+"Cost initial Solution"+" & "+"Cost optimized solution ("+chosen.algorithmType+")"+" & " + "Cost upper bound (100%)"+ " \\\\");
+ if(instance.getOptimalCost() > 0)
+ System.out.println(instance.getName()+" & "+greedySolution.getCost()+" ("+(float)greedySolution.getCost()/(float)instance.getOptimalCost()*100+"%) & "+optimizedSolution.getCost()+" ("+(float)optimizedSolution.getCost()/(float)instance.getOptimalCost()*100+"%) & "+instance.getOptimalCost() + " \\\\");
+ else
+ System.out.println(instance.getName()+" & "+greedySolution.getCost()+" & "+optimizedSolution.getCost()+" & "+ " \\\\");
+ System.out.println("\n Initial solution:\n"+greedySolution.toSol());
+ System.out.println("\n Optimized solution:\n"+optimizedSolution.toSol());
+ }
+
}
}
--
GitLab