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Commit c9529981 authored by Sebastien GOYON's avatar Sebastien GOYON
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Sm agent now also use BaseAgent

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amakaque/MainAmakaque.java deleted 100644 → 0
+ 0
− 161
View file @ a499b2e4
package amakaque;
import java.util.ArrayList;
import java.util.List;
import eval.Eval;
import eval.fun.Rastrigin;
import eval.fun.SchwefelFunction;
import eval.fun.SchwefelFunction1_2;
import eval.fun.StepFunction;
import mas.core.Schedulable;
public class MainAmakaque {
public static void main(String[] args) {
// [PARAM]
int nbrAgent = 50;
// [0.1 : 0.9]
double pr = 0.1;
//
int maxGroup = 5;
int localLimit = 1500;
int globalLimit = 50;
int res = 0;
int nbrTry = 100;
long startTime = System.currentTimeMillis();
for (int i = 0; i < nbrTry; i++) {
res += findSolution(nbrAgent, pr, maxGroup, localLimit, globalLimit);
// System.out.println("run : "+i);
}
long estimatedTime = System.currentTimeMillis() - startTime;
System.out.println("Time : "+estimatedTime/1000+ "s "+estimatedTime%1000+"ms");
// System.out.println(res);
System.out.println("Average cycle : "+(double)res/(double)nbrTry);
// ******************** SCHEDULER ************************
/*
res = 0;
startTime = System.currentTimeMillis();
for (int i = 0; i < nbrTry; i++) {
res += findSolution2(nbrAgent, pr, maxGroup, localLimit, globalLimit);
// System.out.println("run : "+i);
}
estimatedTime = System.currentTimeMillis() - startTime;
System.out.println("Time : "+estimatedTime/1000+ "s "+estimatedTime%1000+"ms");
// System.out.println(res);
System.out.println("Average cycle : "+(double)res/(double)nbrTry);
*/
}
private static int findSolution(
int nbrAgent,
double pr,
int maxGroup,
int localLimit,
int globalLimit
) {
// [INIT]
Eval eval = new StepFunction();
//Eval eval = new SchwefelFunction1_2();
//Eval eval = new SchwefelFunction();
//Eval eval = new Rastrigin();
Env env = new Env(localLimit, globalLimit, maxGroup, eval);
List<SMAgent> agents = new ArrayList<SMAgent>();
for (int i = 0; i < nbrAgent; i++) {
agents.add(new SMAgent(i, pr, env, eval));
}
Group group = new Group();
group.addAgents(agents);
env.initGroup(group);
// [RUN]
int cycle = 0;
while(Math.abs(eval.evaluate(env.getGlobalLeader())-eval.getObjective())>eval.getErrorDelta()) {
do {
for (SMAgent agent : agents) {
agent.perceive();
}
for (SMAgent agent : agents) {
agent.decide();
agent.act();
}
cycle++;
}while (agents.get(0).getPhase() != Phase.LOCAL_LEADER_PHASE);
//cycle ++;
//System.out.println("Cycle : "+cycle+" Value : "+eval.evaluate(env.getGlobalLeader()));
//System.out.println(env.getGlobalLeader());
/*
// Pas à Pas
try {
System.in.read();
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
*/
}
//System.out.println("Best solution : "+env.getGlobalLeader());
//System.out.println("That evaluate to : "+eval.evaluate(env.getGlobalLeader()));
//System.out.println("Eval : "+eval.getCount());
return cycle;
}
private static int findSolution2(
int nbrAgent,
double pr,
int maxGroup,
int localLimit,
int globalLimit) {
// [INIT]
Eval eval = new StepFunction();
//Eval eval = new SchwefelFunction1_2();
//Eval eval = new SchwefelFunction();
//Eval eval = new Rastrigin();
Env env = new Env(localLimit, globalLimit, maxGroup, eval);
List<SMAgent> agents = new ArrayList<SMAgent>();
for (int i = 0; i < nbrAgent; i++) {
agents.add(new SMAgent(i, pr, env, eval));
}
Group group = new Group();
group.addAgents(agents);
env.initGroup(group);
// [RUN]
Scheduler scheduler = new Scheduler(env, eval, agents.toArray(new SMAgent[agents.size()]));
scheduler.start();
scheduler.waitUntilFinish();
return scheduler.getNbOfCycles();
}
}
amakaque/SMAgent.java
+ 20
− 58
View file @ c9529981
...@@ -3,12 +3,13 @@ package amakaque; ...@@ -3,12 +3,13 @@ package amakaque;
import java.util.ArrayList; import java.util.ArrayList;
import java.util.List; import java.util.List;
import baseOptiAgent.BaseAgent;
import eval.Eval; import eval.Eval;
import mas.core.Agent; import mas.core.Agent;
public class SMAgent extends Agent { public class SMAgent extends BaseAgent {
private Env env; private SMEnv env;
/* /*
* Is local leader/ is global leader * Is local leader/ is global leader
...@@ -17,17 +18,11 @@ public class SMAgent extends Agent { ...@@ -17,17 +18,11 @@ public class SMAgent extends Agent {
private boolean isGL; private boolean isGL;
private int currentGroup; private int currentGroup;
private int id;
private List<Double> dimVector;
private double fitness;
private Double pr; private Double pr;
private Phase phase; private Phase phase;
private Eval eval;
// LLP // LLP
private List<Double> randomLocalMember; private List<Double> randomLocalMember;
private List<Double> localLeader; private List<Double> localLeader;
...@@ -63,7 +58,7 @@ public class SMAgent extends Agent { ...@@ -63,7 +58,7 @@ public class SMAgent extends Agent {
/* /*
* Init the SM with random starting point within the env dimension * Init the SM with random starting point within the env dimension
*/ */
public SMAgent(int _id, double _pr, Env _env, Eval _eval) { public SMAgent(int _id, double _pr, SMEnv _env, Eval _eval) {
env = _env; env = _env;
eval = _eval; eval = _eval;
id = _id; id = _id;
...@@ -77,13 +72,9 @@ public class SMAgent extends Agent { ...@@ -77,13 +72,9 @@ public class SMAgent extends Agent {
} }
currentGroup = 0; currentGroup = 0;
dimVector = new ArrayList<Double>(); vector = generateRandomVector();
evaluate = evaluate(vector);
for (int i = 0; i < eval.getDim(); i++) { fitness = fitness(evaluate);
dimVector.add(eval.getMin(i) + Math.random() * (eval.getMax(i) - eval.getMin(i)));
}
fitness = fitness(dimVector);
pr = _pr; pr = _pr;
phase = Phase.LOCAL_LEADER_PHASE; phase = Phase.LOCAL_LEADER_PHASE;
...@@ -102,7 +93,7 @@ public class SMAgent extends Agent { ...@@ -102,7 +93,7 @@ public class SMAgent extends Agent {
randomLocalMember = env.getRandomGroupMember(currentGroup, id); randomLocalMember = env.getRandomGroupMember(currentGroup, id);
globalLeader = env.getGlobalLeader(); globalLeader = env.getGlobalLeader();
groupSize = env.getGroupSize(currentGroup); groupSize = env.getGroupSize(currentGroup);
maxFitness = env.getMaxFitness(); maxFitness = env.getBestFitness();
count = env.getCount(currentGroup); count = env.getCount(currentGroup);
allDone = env.allDone(); allDone = env.allDone();
} }
...@@ -137,16 +128,6 @@ public class SMAgent extends Agent { ...@@ -137,16 +128,6 @@ public class SMAgent extends Agent {
} }
} }
private double fitness(List<Double> values) {
double fun = eval.evaluate(values);
if (fun >= 0) {
return 1/(1 + fun);
}
else {
return 1 + Math.abs(fun);
}
}
private void nextPhase() { private void nextPhase() {
if (phase == Phase.LOCAL_LEADER_PHASE) { if (phase == Phase.LOCAL_LEADER_PHASE) {
...@@ -184,9 +165,9 @@ public class SMAgent extends Agent { ...@@ -184,9 +165,9 @@ public class SMAgent extends Agent {
List<Double> newValues = new ArrayList<Double>(); List<Double> newValues = new ArrayList<Double>();
for (int i = 0; i < dimVector.size(); i++) { for (int i = 0; i < vector.size(); i++) {
Double currentValue = dimVector.get(i); Double currentValue = vector.get(i);
if (Math.random() >= pr) { if (Math.random() >= pr) {
double value = currentValue double value = currentValue
...@@ -209,11 +190,7 @@ public class SMAgent extends Agent { ...@@ -209,11 +190,7 @@ public class SMAgent extends Agent {
* Apply the greedy selection process between existing position and newly generated position, * Apply the greedy selection process between existing position and newly generated position,
* based on fitness and select the better one; * based on fitness and select the better one;
*/ */
double new_fitness = fitness(newValues); compareAndUpdate(newValues);
if (fitness<new_fitness) {
dimVector = newValues;
fitness = new_fitness;
}
} }
private void globalLeaderPhase() { private void globalLeaderPhase() {
...@@ -230,10 +207,10 @@ public class SMAgent extends Agent { ...@@ -230,10 +207,10 @@ public class SMAgent extends Agent {
if (count < groupSize) { if (count < groupSize) {
if (Math.random() < prob) { if (Math.random() < prob) {
env.count(currentGroup); env.count(currentGroup);
int j = (int)(Math.random() * dimVector.size()); int j = (int)(Math.random() * vector.size());
List<Double> newValues = new ArrayList<Double>(dimVector); List<Double> newValues = new ArrayList<Double>(vector);
Double currentValue = dimVector.get(j); Double currentValue = vector.get(j);
double value = currentValue double value = currentValue
+ Math.random() * (globalLeader.get(j) - currentValue) + Math.random() * (globalLeader.get(j) - currentValue)
+ (2*Math.random() - 1) * (randomLocalMember.get(j) - currentValue); + (2*Math.random() - 1) * (randomLocalMember.get(j) - currentValue);
...@@ -246,11 +223,7 @@ public class SMAgent extends Agent { ...@@ -246,11 +223,7 @@ public class SMAgent extends Agent {
newValues.set(j,value); newValues.set(j,value);
double new_fitness = fitness(newValues); compareAndUpdate(newValues);
if (fitness<new_fitness) {
dimVector = newValues;
fitness = new_fitness;
}
} }
} }
} }
...@@ -300,9 +273,9 @@ public class SMAgent extends Agent { ...@@ -300,9 +273,9 @@ public class SMAgent extends Agent {
if (localLimitCount > localLimit) { if (localLimitCount > localLimit) {
List<Double> newValues = new ArrayList<Double>(); List<Double> newValues = new ArrayList<Double>();
for (int j = 0; j < dimVector.size(); j++) { for (int j = 0; j < vector.size(); j++) {
Double currentValue = dimVector.get(j); Double currentValue = vector.get(j);
if (Math.random() >= pr) { if (Math.random() >= pr) {
newValues.add(eval.getMin(j) + Math.random() * (eval.getMax(j) - eval.getMin(j))); newValues.add(eval.getMin(j) + Math.random() * (eval.getMax(j) - eval.getMin(j)));
...@@ -316,8 +289,9 @@ public class SMAgent extends Agent { ...@@ -316,8 +289,9 @@ public class SMAgent extends Agent {
} }
} }
dimVector = newValues; vector = newValues;
fitness = fitness(dimVector); evaluate = evaluate(vector);
fitness = fitness(evaluate);
} }
} }
...@@ -391,14 +365,6 @@ public class SMAgent extends Agent { ...@@ -391,14 +365,6 @@ public class SMAgent extends Agent {
} }
} }
public List<Double> getDimVector() {
return dimVector;
}
public int getId() {
return id;
}
public boolean isLL() { public boolean isLL() {
return isLL; return isLL;
} }
...@@ -429,10 +395,6 @@ public class SMAgent extends Agent { ...@@ -429,10 +395,6 @@ public class SMAgent extends Agent {
return phase; return phase;
} }
public double getFitness() {
return fitness;
}
@Override @Override
public boolean equals(Object obj) { public boolean equals(Object obj) {
if (!(obj instanceof SMAgent)) return false; if (!(obj instanceof SMAgent)) return false;
......
amakaque/Env.java → amakaque/SMEnv.java
+ 23
− 9
View file @ c9529981
...@@ -4,9 +4,10 @@ import java.util.ArrayList; ...@@ -4,9 +4,10 @@ import java.util.ArrayList;
import java.util.List; import java.util.List;
import java.util.Random; import java.util.Random;
import baseOptiAgent.Env;
import eval.Eval; import eval.Eval;
public class Env { public class SMEnv extends Env{
private int globalLimitCount; private int globalLimitCount;
...@@ -15,9 +16,7 @@ public class Env { ...@@ -15,9 +16,7 @@ public class Env {
private int groupLimit; private int groupLimit;
private List<Group> groups; private List<Group> groups;
private Eval eval; public SMEnv(int _localLimit, int _globalLimit, int _groupLimit, Eval _eval) {
public Env(int _localLimit, int _globalLimit, int _groupLimit, Eval _eval) {
globalLimitCount = 0; globalLimitCount = 0;
eval = _eval; eval = _eval;
...@@ -71,7 +70,7 @@ public class Env { ...@@ -71,7 +70,7 @@ public class Env {
randomagent = group.getAgents().get(rand.nextInt(group.getAgents().size())); randomagent = group.getAgents().get(rand.nextInt(group.getAgents().size()));
} }
return randomagent.getDimVector(); return randomagent.getVector();
} }
public List<Double> getLocalLeader(int i_group){ public List<Double> getLocalLeader(int i_group){
...@@ -80,13 +79,14 @@ public class Env { ...@@ -80,13 +79,14 @@ public class Env {
List<SMAgent> agents = group.getAgents(); List<SMAgent> agents = group.getAgents();
for (SMAgent agent: agents) { for (SMAgent agent: agents) {
if(agent.isLL()) { if(agent.isLL()) {
return agent.getDimVector(); return agent.getVector();
} }
} }
throw new java.lang.Error("The group :"+String.valueOf(i_group)+" don t have a local leader."); throw new java.lang.Error("The group :"+String.valueOf(i_group)+" don t have a local leader.");
} }
public double getMaxFitness() { @Override
public double getBestFitness() {
double max = 0; double max = 0;
for (Group group: groups) { for (Group group: groups) {
...@@ -105,7 +105,7 @@ public class Env { ...@@ -105,7 +105,7 @@ public class Env {
for (Group group: groups) { for (Group group: groups) {
for (SMAgent agent: group.getAgents()) { for (SMAgent agent: group.getAgents()) {
if (agent.isGL()) { if (agent.isGL()) {
return agent.getDimVector(); return agent.getVector();
} }
} }
} }
...@@ -230,4 +230,18 @@ public class Env { ...@@ -230,4 +230,18 @@ public class Env {
} }
throw new java.lang.Error("No global leader found."); throw new java.lang.Error("No global leader found.");
} }
@Override
public double getBestEval() {
SMAgent maxAgent = groups.get(0).getAgents().get(0);
for (Group group: groups) {
for (SMAgent agent: group.getAgents()) {
if(agent.getFitness() > maxAgent.getFitness()) {
maxAgent = agent;
}
}
}
return maxAgent.getEvaluate();
}
} }
amakaque/SMSolver.java 0 → 100644
+ 46
− 0
View file @ c9529981
package amakaque;
import java.util.ArrayList;
import java.util.List;
import baseOptiAgent.Result;
import baseOptiAgent.Solver;
import eval.Eval;
public class SMSolver extends Solver{
public SMSolver(Eval _eval, int _maxCycle, int _maxEval) {
super(_eval, _maxCycle, _maxEval);
name = "Spider-Monkey";
}
@Override
public Result solve() {
// [PARAM]
int nbrAgent = 50;
// [0.1 : 0.9]
double pr = 0.1;
//
int maxGroup = 5;
int localLimit = 1500;
int globalLimit = 50;
// [INIT]
SMEnv env = new SMEnv(localLimit, globalLimit, maxGroup, eval);
List<SMAgent> agents = new ArrayList<SMAgent>();
for (int i = 0; i < nbrAgent; i++) {
agents.add(new SMAgent(i, pr, env, eval));
}
Group group = new Group();
group.addAgents(agents);
env.initGroup(group);
return findSolution(agents, env, 6);
}
}
amakaque/Scheduler.java
+ 2
− 2
View file @ c9529981
...@@ -6,11 +6,11 @@ import mas.implementation.schedulers.variations.ThreeStepCycling; ...@@ -6,11 +6,11 @@ import mas.implementation.schedulers.variations.ThreeStepCycling;
public class Scheduler extends ThreeStepCycling{ public class Scheduler extends ThreeStepCycling{
public Env env; public SMEnv env;
public Eval eval; public Eval eval;
public Scheduler(Env _env, Eval _eval, ThreeStepCyclable... _threeStepCyclables) { public Scheduler(SMEnv _env, Eval _eval, ThreeStepCyclable... _threeStepCyclables) {
super(_threeStepCyclables); super(_threeStepCyclables);
env = _env; env = _env;
eval = _eval; eval = _eval;
......
baseOptiAgent/BaseAgent.java
+ 12
− 0
View file @ c9529981
...@@ -66,4 +66,16 @@ public abstract class BaseAgent extends Agent{ ...@@ -66,4 +66,16 @@ public abstract class BaseAgent extends Agent{
return fitness; return fitness;
} }
protected boolean compareAndUpdate(List<Double> otherVec) {
double newEval = evaluate(otherVec);
double newFitness = fitness(newEval);
if (fitness<newFitness) {
vector = otherVec;
fitness = newFitness;
evaluate = newEval;
return true;
}
return false;
}
} }
baseOptiAgent/Env.java
+ 1
− 1
View file @ c9529981
...@@ -4,7 +4,7 @@ import eval.Eval; ...@@ -4,7 +4,7 @@ import eval.Eval;
public abstract class Env { public abstract class Env {
Eval eval; protected Eval eval;
......
baseOptiAgent/Ihm.java
+ 4
− 2
View file @ c9529981
...@@ -4,6 +4,7 @@ import java.io.FileWriter; ...@@ -4,6 +4,7 @@ import java.io.FileWriter;
import java.io.IOException; import java.io.IOException;
import java.util.stream.Stream; import java.util.stream.Stream;
import amakaque.SMSolver;
import ant.AntSolver; import ant.AntSolver;
import bee.BeeSolver; import bee.BeeSolver;
import eval.Eval; import eval.Eval;
...@@ -18,8 +19,9 @@ public class Ihm { ...@@ -18,8 +19,9 @@ public class Ihm {
int maxCycle = 200000; int maxCycle = 200000;
int maxEval = 1000000; int maxEval = 1000000;
Solver solver = new BeeSolver(eval, maxCycle, maxEval); Solver solver = new SMSolver(eval, maxCycle, maxEval);
//Solver solver = new BeeSolver(eval, maxCycle, maxEval);
//Solver solver = new AntSolver(eval, maxCycle, maxEval); //Solver solver = new AntSolver(eval, maxCycle, maxEval);
Result res = solver.solve(); Result res = solver.solve();
......
bee/Bee.java
+ 2
− 11
View file @ c9529981
...@@ -71,18 +71,9 @@ public class Bee extends BaseAgent{ ...@@ -71,18 +71,9 @@ public class Bee extends BaseAgent{
newVector.set(dim, vector.get(dim) + 2 * (Math.random() - 0.5) * (vector.get(dim) - randomAgent.get(dim))); newVector.set(dim, vector.get(dim) + 2 * (Math.random() - 0.5) * (vector.get(dim) - randomAgent.get(dim)));
this.boundValue(newVector); boundValue(newVector);
double newEval = evaluate(newVector); return compareAndUpdate(newVector);
double newFit = fitness(newEval);
if (fitness < newFit) {
//System.out.println("Update from : "+evaluate+" to "+newEval);
vector = newVector;
evaluate = newEval;
fitness = newFit;
return true;
}
return false;
} }
private void employedPhase() { private void employedPhase() {
......
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