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ILS_conf.java
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ILS_conf.java
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//package hh_project;
import AbstractClasses.HyperHeuristic;
import AbstractClasses.ProblemDomain;
import AbstractClasses.ProblemDomain.HeuristicType;
import java.util.ArrayList;
import java.util.Dictionary;
import java.util.Hashtable;
//Commonly referred to as TS-ILS in literature, see Adubi, S. A., Oladipupo, O. O., & Olugbara, O. O. (2021), CEC 2021
public class ILS_conf extends HyperHeuristic{
//heuristics
int[] ls_llh;
int[] mut_llh;
int[] rr_llh;
//evaluations of heuristics, SpeedNew (proposed by Adriaensen et al. 2014) is used..
double[][] eval; //evaluations of LLHs based on SpeedNew
int[][] accepted; //total number of times a LLH's solution was accepted
int[][] timeToSolve; //total duration spent on producing a solution for a LLH
//the alpha and beta values for heuristic type pairings, Thompson Sampling parameters
int r; //immediate reward of a heuristic pair := {0, 1}
int[] alpha;
int[] beta;
double[] prob;
final int W = 200000; //sliding-window size for LLH-type pairings
ArrayList<slidingW> SW; //the sliding window
//concerning parameters
int param0, param1, param2;
int K; //number of heuristics
int[][] HeuristicParamScore;
int[] total_HP_score;
//general variables
int p0, p1;
int i0, i1;
int h0, h1; //the two heuristics to apply
ArrayList<Integer> ls;
int cur = 0, news = 1; //memory locations for current and new solutions
double e_cur, e_news, e_best; //evaluations of the current, new and best solutions respectively
long execTime;
final double[] param = {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0};
private Dictionary hType;
private Dictionary hPos;
private final int types = 2; //the number of heuristic types
//set the pertubation configurations here. See paper referenced (Adubi et al., 2021) in ReadMe for more details
private int[] pairings = {1, 2, 4, 5}; //For example, use {4, 5} if you want single application of either mutation or ruin-recreate heuristics
boolean isPaired; //to note if a pair of heuristic was applied
ProblemDomain problem;
Acceptance_Mechanism accept;
public ILS_conf(long seed, long time){
super(seed);
execTime = time / 1000;
}
private void setup(ProblemDomain objProblem){
hType = new Hashtable();
hPos = new Hashtable();
int pos = 0;
this.problem = objProblem;
ls_llh = problem.getHeuristicsOfType(HeuristicType.LOCAL_SEARCH);
int[] LLH_DOS = problem.getHeuristicsThatUseDepthOfSearch(); //get all the LLHs that use depth_of_search parameter
int[] LLH_IM = problem.getHeuristicsThatUseIntensityOfMutation(); //LLHs that use intensity_of_mutation parameter
for(int i=0; i<ls_llh.length; i++){
hType.put(ls_llh[i], 'n');
hPos.put(ls_llh[i], pos++);
}
mut_llh = problem.getHeuristicsOfType(HeuristicType.MUTATION);
for(int i=0; i<mut_llh.length; i++){
hType.put(mut_llh[i], 'n');
hPos.put(mut_llh[i], pos++);
}
rr_llh = problem.getHeuristicsOfType(HeuristicType.RUIN_RECREATE);
K = ls_llh.length + mut_llh.length + rr_llh.length;
for(int i=0; i<rr_llh.length; i++){
hType.put(rr_llh[i], 'n');
hPos.put(rr_llh[i], pos++);
}
for(int i=0; i<LLH_DOS.length; i++)
hType.put(LLH_DOS[i], 'l');
for(int i=0; i<LLH_IM.length; i++){
try{if((char)hType.get(LLH_IM[i]) == 'l')
hType.put(LLH_IM[i], 'b');
else
hType.put(LLH_IM[i], 'm');}
catch(Exception ex){
//empty
}
}
}
private void init(){
accept = new Acceptance_Mechanism(execTime, new String[]{"FLS"});
total_HP_score = new int[K];
HeuristicParamScore = new int[K][];
param_b_score = new int[param.length];
total_b = param.length;
for(int i=0; i<param.length; i++)
param_b_score[i] = 1;
LS_scores = new int[ls_llh.length];
for(int i=0; i<ls_llh.length; i++)
LS_scores[i] = 1;
LS_sum = ls_llh.length;
transLS = new int[ls_llh.length][];
for(int i=0; i<ls_llh.length; i++)
transLS[i] = new int[ls_llh.length];
for(int i=0; i<ls_llh.length; i++)
for(int j=0; j<ls_llh.length; j++)
transLS[i][j] = 1;
trans_sum = new int[ls_llh.length];
for(int i=0; i<ls_llh.length; i++)
trans_sum[i] = ls_llh.length;
for(int i=0; i<K; i++)
total_HP_score[i] = param.length;
for(int i=0; i<K; i++)
HeuristicParamScore[i] = new int[param.length];
for(int i=0; i<K; i++)
for(int j=0; j<param.length; j++)
HeuristicParamScore[i][j] = 1;
eval = new double[2][];
accepted = new int[2][];
timeToSolve = new int[2][];
eval[0] = new double[mut_llh.length];
eval[1] = new double[rr_llh.length];
accepted[0] = new int[mut_llh.length];
accepted[1] = new int[rr_llh.length];
timeToSolve[0] = new int[mut_llh.length];
timeToSolve[1] = new int[rr_llh.length];
for(int i=0; i<mut_llh.length; i++)
eval[0][i] = Double.MAX_VALUE;
for(int i=0; i<rr_llh.length; i++)
eval[1][i] = Double.MAX_VALUE;
alpha = new int[pairings.length];
beta = new int[pairings.length];
problem.initialiseSolution(cur);
SW = new ArrayList();
e_cur = problem.getFunctionValue(cur);
e_best = e_cur;
}
int no_sampling = 0;
double sample(double a, double b){
double alpha_ = a + b;
double beta_;
double u1, u2, w, v;
if(Math.min(a, b) <= 1.0)
beta_ = Math.max(1/a, 1/b);
else
beta_ = Math.sqrt((alpha_ - 2.0) / (2 * a * b - alpha_));
double gamma = a * 1/beta_;
int no_head_way = 0;
while(true){
u1 = rng.nextDouble();
u2 = rng.nextDouble();
v = beta_ * Math.log(u1 / (1 - u1));
w = a * Math.exp(v);
double tmp = Math.log(alpha_ / (b + w));
double arg1 = alpha_ * tmp + (gamma * v) - 1.3862944;
double arg2 = Math.log(u1 * u1 * u2);
if(arg1 >= arg2)
break;
no_head_way++;
if(no_head_way==25){
no_sampling++;
return (a / (a+b)); //return expected convergence mean
}
}
double x = w / (b + w);
return x;
}
int selectOption(){
double max;
int maxPos;
max = prob[0];
maxPos = 0;
for(int i = 1; i<pairings.length; i++){
if(prob[i] > max){
max = prob[i];
maxPos = i;
}
}
return maxPos;
}
/**
* returns an index of one of the eleven parameters for a parameterised LLH
* @param index an index which represents a LLH type
* @return the index of the parameter selected
*/
int RWS_param(int index){
double total;
double cumm;
if(index > -1){
total = total_HP_score[index];
cumm = HeuristicParamScore[index][0];
double threshold = rng.nextDouble() * total;
int i = 0;
while(cumm < threshold){
i++;
cumm += HeuristicParamScore[index][i];
}
return i;
}
else{
total = total_b;
cumm = param_b_score[0];
double threshold = rng.nextDouble() * total;
int i = 0;
while(cumm < threshold){
i++;
cumm += param_b_score[i];
}
return i;
}
}
int RWS(int heuType, int pos){
int[] h;
switch(heuType)
{
case 0:
h = mut_llh;
break;
case 1:
h = rr_llh;
break;
default:
h = ls_llh;
break;
}
double total = 0;
for(int i = 0; i < eval[heuType].length; i++)
total += eval[heuType][i];
double threshold = rng.nextDouble() * total;
int opt = 0;
double cumm = eval[heuType][0];
while(cumm < threshold){
opt++;
cumm += eval[heuType][opt];
}
if(pos == 0)
h0 = h[opt];
else
h1 = h[opt];
return opt;
}
double apply(int h){
int heu, s0, s1;
double e1; //new evaluation
if(h == 0){
heu = h0;
s0 = cur;
}
else{
heu = h1;
s0 = news;
}
s1 = news;
e1 = problem.applyHeuristic(heu, s0, s1);
return e1;
}
void setParam(int heu, int pos){
//if the first type is a MUT or RR LLH
int paramVal;
int index = (int)hPos.get(heu);
switch(pos){
case 0:
paramVal = RWS_param(index);
problem.setIntensityOfMutation(param[paramVal]);
param0 = paramVal;
break;
case 1:
paramVal = RWS_param(index);
problem.setIntensityOfMutation(param[paramVal]);
param1 = paramVal;
break;
case 2: //if LS heuristics called the method
param2 = RWS_param(index); //LS parameters occupy row 2 (third row) of the parameter matrix
problem.setDepthOfSearch(param[param2]);
break;
default:
param_b = RWS_param(-1);
problem.setDepthOfSearch(param[param_b]);
break;
}
}
int param_b;
int[] param_b_score;
int total_b;
void updateParam(){
if((char)hType.get(h0) != 'n'){
int x = (int)hPos.get(h0);
HeuristicParamScore[x][param0]++;
total_HP_score[x]++;
if((char)hType.get(h0) == 'b'){
param_b_score[param_b]++;
total_b++;
}
}
//update the parameter settings of the second heuristic only if it was paired with the first
if(isPaired){
if((char)hType.get(h1) != 'n'){
int x = (int)hPos.get(h1);
HeuristicParamScore[x][param1]++;
total_HP_score[x]++;
if((char)hType.get(h1) == 'b' && h0 != h1){
param_b_score[param_b]++;
total_b++;
}
}
}
//take care the LS parameters
if(r_list.size() == 1){
int x;
int heu = ls_llh[r_list.get(0)];
x = (int)hPos.get(heu);
HeuristicParamScore[x][p_list.get(0)]++;
total_HP_score[x]++;
LS_scores[r_list.get(0)]++;
LS_sum++;
}
else if(r_list.size() > 1){
int x;
for(int i=0; i<r_list.size(); i++){
int p1 = r_list.get(i);
if(i+1 < r_list.size()){
int p2 = r_list.get(i+1);
transLS[p1][p2]++;
trans_sum[p1]++;
}
int h = ls_llh[p1];
x = (int)hPos.get(h);
LS_scores[p1]++;
LS_sum++;
HeuristicParamScore[x][p_list.get(i)]++;
total_HP_score[x]++;
}
}
}
/**
* The method solve which implements the main logic of the HH
* @param objProblem represents the problem domain object
*/
@Override
protected void solve(ProblemDomain objProblem){
setup(objProblem);
init();
prob = new double[pairings.length];
while(!hasTimeExpired()){
r = 0;
for(int i=0; i<pairings.length; i++)
prob[i] = sample(alpha[i] + 1, beta[i] + 1);
int option = selectOption(); //select the pair with the maximum reward, BP
int pairValue = pairings[option];
p0 = pairValue / types;
p1 = pairValue % types;
if(p0 < types){
i0 = RWS(p0, 0);
i1 = RWS(p1, 1);
}
else
i0 = RWS(p1, 0);
if((char)hType.get(h0) != 'n')
setParam(h0, 0);
if((char)hType.get(h0) == 'b')
setParam(h0, -1);
long start = getElapsedTime(); //record the time before application of heuristics
e_news = apply(0);
hasTimeExpired();
if(p0 == types){ //no double shaking
LS();
isPaired = false;
p0 = p1; //in order to use the right row of the matrix..
//param0 = param1;
}
else{
if((char)hType.get(h1) != 'n')
setParam(h1, 1);
if((char)hType.get(h1) == 'b' && h0 != h1)
setParam(h1, -1);
e_news = apply(1);
hasTimeExpired();
LS();
isPaired = true;
}
long elapsedTime = getElapsedTime();
long solveTime = elapsedTime - start + 1;
//if(isPaired) solveTime /= 2;
timeToSolve[p0][i0] += solveTime;
if(h0 != h1 && isPaired)
timeToSolve[p1][i1] += solveTime;
if(!problem.compareSolutions(news, cur) && accept.accept(e_news, e_cur, e_best, getElapsedTime())){
accepted[p0][i0]++;
if(h0 != h1 && isPaired)
accepted[p1][i1]++;
problem.copySolution(news, cur);
e_cur = e_news;
if(e_cur < e_best){
r = 1; //BP
e_best = e_cur;
/*if(isPaired)
pairs.add(h0 + ", " + h1);*/
updateParam();
}
}
updateTS(option); //update alpha and beta parameters for the just applied heuristic pair
eval[p0][i0] = (1.0 + accepted[p0][i0])/timeToSolve[p0][i0];
if(h0 != h1 && isPaired) //BP
eval[p1][i1] = (1.0 + accepted[p1][i1])/timeToSolve[p1][i1]; //edited
param0 = param1 = param2 = param_b = -1;
}
System.out.println("Sampling could not be applied: " + no_sampling + " time(s)");
System.out.println("hType-P run's ended");
}
/**
* Updates the alpha and beta parameters of a heuristic-type pair after application
* @param pI the index of the pair
*/
private void updateTS(int pI){
slidingW tempW = new slidingW();
tempW.h = pI;
tempW.reward = r;
alpha[pI] += r;
beta[pI] += 1-r;
SW.add(tempW);
if(SW.size() > W){
//insert and update the LLH reward
int h_stored = SW.get(0).h;
int r_stored = SW.get(0).reward;
if(r_stored > 0)
alpha[h_stored]--;
else
beta[h_stored]--;
SW.remove(0);
}
}
int RWS_LS(int prev){
int[] score;
int total;
if(prev == -1){
score = LS_scores;
total = LS_sum;
}
else{
score = transLS[prev];
total = trans_sum[prev];
}
double threshold = rng.nextDouble() * total;
int i = 0;
double cumm = score[i];
while(cumm < threshold){
i++;
cumm += score[i];
}
return i;
}
ArrayList<Integer> r_list = new ArrayList();
ArrayList<Integer> p_list = new ArrayList();
int[] LS_scores;
int LS_sum;
int[][] transLS; int[] trans_sum;
private void LS(){
r_list.clear(); //clear the reward list
p_list.clear();
double e_ls; //temporary local search evaluation
int h_ls;
int i_cur, i_prev = -1;
i_cur = RWS_LS(i_prev);
h_ls = ls_llh[i_cur];
setParam(h_ls, 2); //set the parameters for the Local Search heuristic
e_ls = problem.applyHeuristic(h_ls, news, news);
hasTimeExpired();
while(e_ls < e_news){
r_list.add(i_cur);
p_list.add(param2);
e_news = e_ls;
i_prev = i_cur;
i_cur = RWS_LS(i_prev);
h_ls = ls_llh[i_cur];
setParam(h_ls, 2); //set the parameters for the Local Search heuristic
e_ls = problem.applyHeuristic(h_ls, news, news);
hasTimeExpired();
}
}
@Override
public String toString() {
String ext = "";
for(int i=0; i<pairings.length; i++){
if(i == pairings.length - 1)
ext += pairings[i] + "";
else
ext += pairings[i] + ", ";
}
return String.format("hType-P {%s}", ext);
}
}