From c952998120ca45ad71d994c47ac5edc5b494943a Mon Sep 17 00:00:00 2001
From: Sebastien GOYON <shinedday@gmail.com>
Date: Wed, 6 Jul 2022 08:57:34 +0200
Subject: [PATCH] Sm agent now also use BaseAgent
---
amakaque/MainAmakaque.java | 161 ------------------------------
amakaque/SMAgent.java | 78 ++++-----------
amakaque/{Env.java => SMEnv.java} | 32 ++++--
amakaque/SMSolver.java | 46 +++++++++
amakaque/Scheduler.java | 4 +-
baseOptiAgent/BaseAgent.java | 12 +++
baseOptiAgent/Env.java | 2 +-
baseOptiAgent/Ihm.java | 6 +-
bee/Bee.java | 13 +--
9 files changed, 110 insertions(+), 244 deletions(-)
delete mode 100644 amakaque/MainAmakaque.java
rename amakaque/{Env.java => SMEnv.java} (88%)
create mode 100644 amakaque/SMSolver.java
diff --git a/amakaque/MainAmakaque.java b/amakaque/MainAmakaque.java
deleted file mode 100644
index e23ac64..0000000
--- a/amakaque/MainAmakaque.java
+++ /dev/null
@@ -1,161 +0,0 @@
-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();
- }
-
-}
diff --git a/amakaque/SMAgent.java b/amakaque/SMAgent.java
index f77252e..f426120 100644
--- a/amakaque/SMAgent.java
+++ b/amakaque/SMAgent.java
@@ -3,12 +3,13 @@ package amakaque;
import java.util.ArrayList;
import java.util.List;
+import baseOptiAgent.BaseAgent;
import eval.Eval;
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
@@ -17,17 +18,11 @@ public class SMAgent extends Agent {
private boolean isGL;
private int currentGroup;
- private int id;
-
- private List<Double> dimVector;
- private double fitness;
private Double pr;
private Phase phase;
- private Eval eval;
-
// LLP
private List<Double> randomLocalMember;
private List<Double> localLeader;
@@ -63,7 +58,7 @@ public class SMAgent extends Agent {
/*
* 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;
eval = _eval;
id = _id;
@@ -77,13 +72,9 @@ public class SMAgent extends Agent {
}
currentGroup = 0;
- dimVector = new ArrayList<Double>();
-
- for (int i = 0; i < eval.getDim(); i++) {
- dimVector.add(eval.getMin(i) + Math.random() * (eval.getMax(i) - eval.getMin(i)));
- }
-
- fitness = fitness(dimVector);
+ vector = generateRandomVector();
+ evaluate = evaluate(vector);
+ fitness = fitness(evaluate);
pr = _pr;
phase = Phase.LOCAL_LEADER_PHASE;
@@ -102,7 +93,7 @@ public class SMAgent extends Agent {
randomLocalMember = env.getRandomGroupMember(currentGroup, id);
globalLeader = env.getGlobalLeader();
groupSize = env.getGroupSize(currentGroup);
- maxFitness = env.getMaxFitness();
+ maxFitness = env.getBestFitness();
count = env.getCount(currentGroup);
allDone = env.allDone();
}
@@ -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() {
if (phase == Phase.LOCAL_LEADER_PHASE) {
@@ -184,9 +165,9 @@ public class SMAgent extends Agent {
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) {
double value = currentValue
@@ -209,11 +190,7 @@ public class SMAgent extends Agent {
* Apply the greedy selection process between existing position and newly generated position,
* based on fitness and select the better one;
*/
- double new_fitness = fitness(newValues);
- if (fitness<new_fitness) {
- dimVector = newValues;
- fitness = new_fitness;
- }
+ compareAndUpdate(newValues);
}
private void globalLeaderPhase() {
@@ -230,10 +207,10 @@ public class SMAgent extends Agent {
if (count < groupSize) {
if (Math.random() < prob) {
env.count(currentGroup);
- int j = (int)(Math.random() * dimVector.size());
+ int j = (int)(Math.random() * vector.size());
- List<Double> newValues = new ArrayList<Double>(dimVector);
- Double currentValue = dimVector.get(j);
+ List<Double> newValues = new ArrayList<Double>(vector);
+ Double currentValue = vector.get(j);
double value = currentValue
+ Math.random() * (globalLeader.get(j) - currentValue)
+ (2*Math.random() - 1) * (randomLocalMember.get(j) - currentValue);
@@ -246,11 +223,7 @@ public class SMAgent extends Agent {
newValues.set(j,value);
- double new_fitness = fitness(newValues);
- if (fitness<new_fitness) {
- dimVector = newValues;
- fitness = new_fitness;
- }
+ compareAndUpdate(newValues);
}
}
}
@@ -300,9 +273,9 @@ public class SMAgent extends Agent {
if (localLimitCount > localLimit) {
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) {
newValues.add(eval.getMin(j) + Math.random() * (eval.getMax(j) - eval.getMin(j)));
@@ -316,8 +289,9 @@ public class SMAgent extends Agent {
}
}
- dimVector = newValues;
- fitness = fitness(dimVector);
+ vector = newValues;
+ evaluate = evaluate(vector);
+ fitness = fitness(evaluate);
}
}
@@ -391,14 +365,6 @@ public class SMAgent extends Agent {
}
}
- public List<Double> getDimVector() {
- return dimVector;
- }
-
- public int getId() {
- return id;
- }
-
public boolean isLL() {
return isLL;
}
@@ -429,10 +395,6 @@ public class SMAgent extends Agent {
return phase;
}
- public double getFitness() {
- return fitness;
- }
-
@Override
public boolean equals(Object obj) {
if (!(obj instanceof SMAgent)) return false;
diff --git a/amakaque/Env.java b/amakaque/SMEnv.java
similarity index 88%
rename from amakaque/Env.java
rename to amakaque/SMEnv.java
index a7d2b1b..b4c811e 100644
--- a/amakaque/Env.java
+++ b/amakaque/SMEnv.java
@@ -4,9 +4,10 @@ import java.util.ArrayList;
import java.util.List;
import java.util.Random;
+import baseOptiAgent.Env;
import eval.Eval;
-public class Env {
+public class SMEnv extends Env{
private int globalLimitCount;
@@ -15,9 +16,7 @@ public class Env {
private int groupLimit;
private List<Group> groups;
- private Eval eval;
-
- public Env(int _localLimit, int _globalLimit, int _groupLimit, Eval _eval) {
+ public SMEnv(int _localLimit, int _globalLimit, int _groupLimit, Eval _eval) {
globalLimitCount = 0;
eval = _eval;
@@ -71,7 +70,7 @@ public class Env {
randomagent = group.getAgents().get(rand.nextInt(group.getAgents().size()));
}
- return randomagent.getDimVector();
+ return randomagent.getVector();
}
public List<Double> getLocalLeader(int i_group){
@@ -80,13 +79,14 @@ public class Env {
List<SMAgent> agents = group.getAgents();
for (SMAgent agent: agents) {
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.");
}
-
- public double getMaxFitness() {
+
+ @Override
+ public double getBestFitness() {
double max = 0;
for (Group group: groups) {
@@ -105,7 +105,7 @@ public class Env {
for (Group group: groups) {
for (SMAgent agent: group.getAgents()) {
if (agent.isGL()) {
- return agent.getDimVector();
+ return agent.getVector();
}
}
}
@@ -230,4 +230,18 @@ public class Env {
}
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();
+ }
}
diff --git a/amakaque/SMSolver.java b/amakaque/SMSolver.java
new file mode 100644
index 0000000..7a1ed05
--- /dev/null
+++ b/amakaque/SMSolver.java
@@ -0,0 +1,46 @@
+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);
+ }
+}
diff --git a/amakaque/Scheduler.java b/amakaque/Scheduler.java
index bb0b501..997aede 100644
--- a/amakaque/Scheduler.java
+++ b/amakaque/Scheduler.java
@@ -6,11 +6,11 @@ import mas.implementation.schedulers.variations.ThreeStepCycling;
public class Scheduler extends ThreeStepCycling{
- public Env env;
+ public SMEnv env;
public Eval eval;
- public Scheduler(Env _env, Eval _eval, ThreeStepCyclable... _threeStepCyclables) {
+ public Scheduler(SMEnv _env, Eval _eval, ThreeStepCyclable... _threeStepCyclables) {
super(_threeStepCyclables);
env = _env;
eval = _eval;
diff --git a/baseOptiAgent/BaseAgent.java b/baseOptiAgent/BaseAgent.java
index 173cc3d..6f3cc37 100644
--- a/baseOptiAgent/BaseAgent.java
+++ b/baseOptiAgent/BaseAgent.java
@@ -66,4 +66,16 @@ public abstract class BaseAgent extends Agent{
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;
+ }
+
}
diff --git a/baseOptiAgent/Env.java b/baseOptiAgent/Env.java
index 12c8fd9..dad602c 100644
--- a/baseOptiAgent/Env.java
+++ b/baseOptiAgent/Env.java
@@ -4,7 +4,7 @@ import eval.Eval;
public abstract class Env {
- Eval eval;
+ protected Eval eval;
diff --git a/baseOptiAgent/Ihm.java b/baseOptiAgent/Ihm.java
index ac5b138..5306e9c 100644
--- a/baseOptiAgent/Ihm.java
+++ b/baseOptiAgent/Ihm.java
@@ -4,6 +4,7 @@ import java.io.FileWriter;
import java.io.IOException;
import java.util.stream.Stream;
+import amakaque.SMSolver;
import ant.AntSolver;
import bee.BeeSolver;
import eval.Eval;
@@ -18,8 +19,9 @@ public class Ihm {
int maxCycle = 200000;
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);
Result res = solver.solve();
diff --git a/bee/Bee.java b/bee/Bee.java
index 8bd81cf..a1ee2ce 100644
--- a/bee/Bee.java
+++ b/bee/Bee.java
@@ -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)));
- this.boundValue(newVector);
+ boundValue(newVector);
- double newEval = evaluate(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;
+ return compareAndUpdate(newVector);
}
private void employedPhase() {
--
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