1 |
|
/* |
2 |
< |
* Written by Bill Scherer and Doug Lea with assistance from members |
3 |
< |
* of JCP JSR-166 Expert Group and released to the public domain. Use, |
4 |
< |
* modify, and redistribute this code in any way without |
5 |
< |
* acknowledgement. |
2 |
> |
* Written by Doug Lea and Bill Scherer with assistance from members |
3 |
> |
* of JCP JSR-166 Expert Group and released to the public domain, as |
4 |
> |
* explained at http://creativecommons.org/publicdomain/zero/1.0/ |
5 |
|
*/ |
6 |
|
|
7 |
+ |
import java.util.*; |
8 |
|
import java.util.concurrent.*; |
9 |
|
import java.util.concurrent.atomic.*; |
10 |
|
import java.util.concurrent.locks.*; |
11 |
|
|
12 |
+ |
/** |
13 |
+ |
* A parallel Traveling Salesperson Problem (TSP) program based on a |
14 |
+ |
* genetic algorithm using an Exchanger. A population of chromosomes is |
15 |
+ |
* distributed among "subpops". Each chromosomes represents a tour, |
16 |
+ |
* and its fitness is the total tour length. |
17 |
+ |
* |
18 |
+ |
* A set of worker threads perform updates on subpops. The basic |
19 |
+ |
* update step is: |
20 |
+ |
* <ol> |
21 |
+ |
* <li> Select a breeder b from the subpop |
22 |
+ |
* <li> Create a strand of its tour with a random starting point and length |
23 |
+ |
* <li> Offer the strand to the exchanger, receiving a strand from |
24 |
+ |
* another subpop |
25 |
+ |
* <li> Combine b and the received strand using crossing function to |
26 |
+ |
* create new chromosome c. |
27 |
+ |
* <li> Replace a chromosome in the subpop with c. |
28 |
+ |
* </ol> |
29 |
+ |
* |
30 |
+ |
* This continues for a given number of generations per subpop. |
31 |
+ |
* Because there are normally more subpops than threads, each worker |
32 |
+ |
* thread performs small (randomly sized) run of updates for one |
33 |
+ |
* subpop and then selects another. A run continues until there is at |
34 |
+ |
* most one remaining thread performing updates. |
35 |
+ |
* |
36 |
+ |
* See below for more details. |
37 |
+ |
*/ |
38 |
|
public class TSPExchangerTest { |
39 |
< |
// Set SLS true to use as default the settings in Scherer, Lea, and |
40 |
< |
// Scott paper. Otherwise smaller values are used to speed up testing |
41 |
< |
static final boolean SLS = false; |
42 |
< |
|
43 |
< |
static final int DEFAULT_THREADS = SLS? 32: 8; |
44 |
< |
static final int DEFAULT_CITIES = SLS? 100: 50; |
45 |
< |
static final int DEFAULT_POPULATION = SLS? 1000: 500; |
46 |
< |
static final int DEFAULT_BREEDERS = SLS? 200: 100; |
47 |
< |
static final int DEFAULT_GENERATIONS = SLS? 20000: 10000; |
39 |
> |
static final int NCPUS = Runtime.getRuntime().availableProcessors(); |
40 |
> |
|
41 |
> |
/** Runs start with two threads, increasing by two through max */ |
42 |
> |
static final int DEFAULT_MAX_THREADS = Math.max(4, NCPUS + NCPUS/2); |
43 |
> |
|
44 |
> |
/** The number of replication runs per thread value */ |
45 |
> |
static final int DEFAULT_REPLICATIONS = 3; |
46 |
> |
|
47 |
> |
/** If true, print statistics in SNAPSHOT_RATE intervals */ |
48 |
> |
static boolean verbose = true; |
49 |
> |
static final long SNAPSHOT_RATE = 10000; // in milliseconds |
50 |
> |
|
51 |
> |
/** |
52 |
> |
* The problem size. Each city is a random point. The goal is to |
53 |
> |
* find a tour among them with smallest total Euclidean distance. |
54 |
> |
*/ |
55 |
> |
static final int DEFAULT_CITIES = 144; |
56 |
> |
|
57 |
> |
// Tuning parameters. |
58 |
> |
|
59 |
> |
/** |
60 |
> |
* The number of chromosomes per subpop. Must be a power of two. |
61 |
> |
* |
62 |
> |
* Smaller values lead to faster iterations but poorer quality |
63 |
> |
* results |
64 |
> |
*/ |
65 |
> |
static final int DEFAULT_SUBPOP_SIZE = 32; |
66 |
> |
|
67 |
> |
/** |
68 |
> |
* The number of iterations per subpop. Convergence appears |
69 |
> |
* to be roughly proportional to #cities-squared |
70 |
> |
*/ |
71 |
> |
static final int DEFAULT_GENERATIONS = DEFAULT_CITIES * DEFAULT_CITIES; |
72 |
> |
|
73 |
> |
/** |
74 |
> |
* The number of subpops. The total population is #subpops * subpopSize, |
75 |
> |
* which should be roughly on the order of #cities-squared |
76 |
> |
* |
77 |
> |
* Smaller values lead to faster total runs but poorer quality |
78 |
> |
* results |
79 |
> |
*/ |
80 |
> |
static final int DEFAULT_NSUBPOPS = DEFAULT_GENERATIONS / DEFAULT_SUBPOP_SIZE; |
81 |
> |
|
82 |
> |
/** |
83 |
> |
* The minimum length for a random chromosome strand. |
84 |
> |
* Must be at least 1. |
85 |
> |
*/ |
86 |
> |
static final int MIN_STRAND_LENGTH = 3; |
87 |
> |
|
88 |
> |
/** |
89 |
> |
* The probability mask value for creating random strands, |
90 |
> |
* that have lengths at least MIN_STRAND_LENGTH, and grow |
91 |
> |
* with exponential decay 2^(-(1/(RANDOM_STRAND_MASK + 1) |
92 |
> |
* Must be 1 less than a power of two. |
93 |
> |
*/ |
94 |
> |
static final int RANDOM_STRAND_MASK = 7; |
95 |
> |
|
96 |
> |
/** |
97 |
> |
* Probability control for selecting breeders. |
98 |
> |
* Breeders are selected starting at the best-fitness chromosome, |
99 |
> |
* with exponentially decaying probability |
100 |
> |
* 1 / (subpopSize >>> BREEDER_DECAY). |
101 |
> |
* |
102 |
> |
* Larger values usually cause faster convergence but poorer |
103 |
> |
* quality results |
104 |
> |
*/ |
105 |
> |
static final int BREEDER_DECAY = 1; |
106 |
> |
|
107 |
> |
/** |
108 |
> |
* Probability control for selecting dyers. |
109 |
> |
* Dyers are selected starting at the worst-fitness chromosome, |
110 |
> |
* with exponentially decaying probability |
111 |
> |
* 1 / (subpopSize >>> DYER_DECAY) |
112 |
> |
* |
113 |
> |
* Larger values usually cause faster convergence but poorer |
114 |
> |
* quality results |
115 |
> |
*/ |
116 |
> |
static final int DYER_DECAY = 1; |
117 |
|
|
118 |
+ |
/** |
119 |
+ |
* The set of cities. Created once per program run, to |
120 |
+ |
* make it easier to compare solutions across different runs. |
121 |
+ |
*/ |
122 |
+ |
static CitySet cities; |
123 |
|
|
124 |
|
public static void main(String[] args) throws Exception { |
125 |
< |
int maxThreads = DEFAULT_THREADS; |
125 |
> |
int maxThreads = DEFAULT_MAX_THREADS; |
126 |
|
int nCities = DEFAULT_CITIES; |
127 |
< |
int pSize = DEFAULT_POPULATION; |
128 |
< |
int nBreeders = DEFAULT_BREEDERS; |
129 |
< |
int numGenerations = DEFAULT_GENERATIONS; |
127 |
> |
int subpopSize = DEFAULT_SUBPOP_SIZE; |
128 |
> |
int nGen = nCities * nCities; |
129 |
> |
int nSubpops = nCities * nCities / subpopSize; |
130 |
> |
int nReps = DEFAULT_REPLICATIONS; |
131 |
|
|
31 |
– |
// Parse and check args |
32 |
– |
int argc = 0; |
132 |
|
try { |
133 |
+ |
int argc = 0; |
134 |
|
while (argc < args.length) { |
135 |
|
String option = args[argc++]; |
136 |
< |
if (option.equals("-b")) |
37 |
< |
nBreeders = Integer.parseInt(args[argc]); |
38 |
< |
else if (option.equals("-c")) |
136 |
> |
if (option.equals("-c")) { |
137 |
|
nCities = Integer.parseInt(args[argc]); |
138 |
+ |
nGen = nCities * nCities; |
139 |
+ |
nSubpops = nCities * nCities / subpopSize; |
140 |
+ |
} |
141 |
+ |
else if (option.equals("-p")) |
142 |
+ |
subpopSize = Integer.parseInt(args[argc]); |
143 |
|
else if (option.equals("-g")) |
144 |
< |
numGenerations = Integer.parseInt(args[argc]); |
145 |
< |
else if (option.equals("-p")) |
146 |
< |
pSize = Integer.parseInt(args[argc]); |
147 |
< |
else |
144 |
> |
nGen = Integer.parseInt(args[argc]); |
145 |
> |
else if (option.equals("-n")) |
146 |
> |
nSubpops = Integer.parseInt(args[argc]); |
147 |
> |
else if (option.equals("-q")) { |
148 |
> |
verbose = false; |
149 |
> |
argc--; |
150 |
> |
} |
151 |
> |
else if (option.equals("-r")) |
152 |
> |
nReps = Integer.parseInt(args[argc]); |
153 |
> |
else |
154 |
|
maxThreads = Integer.parseInt(option); |
155 |
|
argc++; |
156 |
|
} |
157 |
|
} |
49 |
– |
catch (NumberFormatException e) { |
50 |
– |
reportUsageErrorAndDie(); |
51 |
– |
System.exit(0); |
52 |
– |
} |
158 |
|
catch (Exception e) { |
159 |
|
reportUsageErrorAndDie(); |
160 |
|
} |
161 |
|
|
162 |
< |
// Display runtime parameters |
163 |
< |
System.out.print("TSPExchangerTest -b " + nBreeders); |
164 |
< |
System.out.print(" -c " + nCities); |
165 |
< |
System.out.print(" -g " + numGenerations); |
166 |
< |
System.out.print(" -p " + pSize); |
167 |
< |
System.out.print(" max threads " + maxThreads); |
168 |
< |
System.out.println(); |
169 |
< |
|
170 |
< |
// warmup |
171 |
< |
System.out.print("Threads: " + 2 + "\t"); |
172 |
< |
oneRun(2, |
173 |
< |
nCities, |
174 |
< |
pSize, |
175 |
< |
nBreeders, |
176 |
< |
numGenerations); |
177 |
< |
Thread.sleep(100); |
178 |
< |
|
179 |
< |
int k = 4; |
180 |
< |
for (int i = 2; i <= maxThreads;) { |
181 |
< |
System.out.print("Threads: " + i + "\t"); |
182 |
< |
oneRun(i, |
183 |
< |
nCities, |
184 |
< |
pSize, |
185 |
< |
nBreeders, |
186 |
< |
numGenerations); |
82 |
< |
Thread.sleep(100); |
83 |
< |
if (i == k) { |
84 |
< |
k = i << 1; |
85 |
< |
i = i + (i >>> 1); |
86 |
< |
} |
87 |
< |
else |
88 |
< |
i = k; |
162 |
> |
System.out.print("TSPExchangerTest"); |
163 |
> |
System.out.print(" -c " + nCities); |
164 |
> |
System.out.print(" -g " + nGen); |
165 |
> |
System.out.print(" -p " + subpopSize); |
166 |
> |
System.out.print(" -n " + nSubpops); |
167 |
> |
System.out.print(" -r " + nReps); |
168 |
> |
System.out.print(" max threads " + maxThreads); |
169 |
> |
System.out.println(); |
170 |
> |
|
171 |
> |
cities = new CitySet(nCities); |
172 |
> |
|
173 |
> |
if (false && NCPUS > 4) { |
174 |
> |
int h = NCPUS/2; |
175 |
> |
System.out.printf("Threads: %4d Warmup\n", h); |
176 |
> |
oneRun(h, nSubpops, subpopSize, nGen); |
177 |
> |
Thread.sleep(500); |
178 |
> |
} |
179 |
> |
|
180 |
> |
int maxt = (maxThreads < nSubpops) ? maxThreads : nSubpops; |
181 |
> |
for (int j = 0; j < nReps; ++j) { |
182 |
> |
for (int i = 2; i <= maxt; i += 2) { |
183 |
> |
System.out.printf("Threads: %4d Replication: %2d\n", i, j); |
184 |
> |
oneRun(i, nSubpops, subpopSize, nGen); |
185 |
> |
Thread.sleep(500); |
186 |
> |
} |
187 |
|
} |
188 |
|
} |
189 |
|
|
190 |
< |
private static void reportUsageErrorAndDie() { |
191 |
< |
System.out.print("usage: TSPExchangerTest [-b #breeders] [-c #cities]"); |
192 |
< |
System.out.println(" [-g #generations]"); |
193 |
< |
System.out.println(" [-p population size] [ #threads]"); |
190 |
> |
static void reportUsageErrorAndDie() { |
191 |
> |
System.out.print("usage: TSPExchangerTest"); |
192 |
> |
System.out.print(" [-c #cities]"); |
193 |
> |
System.out.print(" [-p #subpopSize]"); |
194 |
> |
System.out.print(" [-g #generations]"); |
195 |
> |
System.out.print(" [-n #subpops]"); |
196 |
> |
System.out.print(" [-r #replications]"); |
197 |
> |
System.out.print(" [-q <quiet>]"); |
198 |
> |
System.out.print(" #threads]"); |
199 |
> |
System.out.println(); |
200 |
|
System.exit(0); |
201 |
|
} |
202 |
|
|
203 |
< |
static void oneRun(int nThreads, |
204 |
< |
int nCities, |
205 |
< |
int pSize, |
206 |
< |
int nBreeders, |
207 |
< |
int numGenerations) |
208 |
< |
throws Exception { |
209 |
< |
CyclicBarrier runBarrier = new CyclicBarrier(nThreads + 1); |
210 |
< |
Population p = new Population(nCities, pSize, nBreeders, nThreads, |
211 |
< |
numGenerations, runBarrier); |
212 |
< |
|
213 |
< |
// Run the test |
214 |
< |
long startTime = System.currentTimeMillis(); |
215 |
< |
runBarrier.await(); // start 'em off |
216 |
< |
runBarrier.await(); // wait 'til they're done |
217 |
< |
long stopTime = System.currentTimeMillis(); |
218 |
< |
long elapsed = stopTime - startTime; |
219 |
< |
long rate = (numGenerations * 1000) / elapsed; |
220 |
< |
double secs = (double)elapsed / 1000.0; |
203 |
> |
/** |
204 |
> |
* Performs one run with the given parameters. Each run completes |
205 |
> |
* when there are fewer than 2 active threads. When there is |
206 |
> |
* only one remaining thread, it will have no one to exchange |
207 |
> |
* with, so it is terminated (via interrupt). |
208 |
> |
*/ |
209 |
> |
static void oneRun(int nThreads, int nSubpops, int subpopSize, int nGen) |
210 |
> |
throws InterruptedException { |
211 |
> |
Population p = new Population(nThreads, nSubpops, subpopSize, nGen); |
212 |
> |
ProgressMonitor mon = null; |
213 |
> |
if (verbose) { |
214 |
> |
p.printSnapshot(0); |
215 |
> |
mon = new ProgressMonitor(p); |
216 |
> |
mon.start(); |
217 |
> |
} |
218 |
> |
long startTime = System.nanoTime(); |
219 |
> |
p.start(); |
220 |
> |
p.awaitDone(); |
221 |
> |
long stopTime = System.nanoTime(); |
222 |
> |
if (mon != null) |
223 |
> |
mon.interrupt(); |
224 |
> |
p.shutdown(); |
225 |
> |
// Thread.sleep(100); |
226 |
|
|
227 |
< |
// Display results |
228 |
< |
System.out.print(LoopHelpers.rightJustify((int)p.bestFitness()) + |
229 |
< |
" fitness"); |
121 |
< |
System.out.print(LoopHelpers.rightJustify(rate) + " gen/s \t"); |
122 |
< |
System.out.print(secs + "s elapsed"); |
123 |
< |
System.out.println(); |
227 |
> |
long elapsed = stopTime - startTime; |
228 |
> |
double secs = (double) elapsed / 1000000000.0; |
229 |
> |
p.printSnapshot(secs); |
230 |
|
} |
231 |
|
|
232 |
+ |
/** |
233 |
+ |
* A Population creates the subpops, subpops, and threads for a run |
234 |
+ |
* and has control methods to start, stop, and report progress. |
235 |
+ |
*/ |
236 |
|
static final class Population { |
237 |
< |
final Chromosome[] individuals; |
238 |
< |
final Exchanger<Chromosome> x; |
239 |
< |
final CitySet cities; |
240 |
< |
final int[] dyers; |
241 |
< |
final int[] breeders; |
242 |
< |
final CyclicBarrier generationBarrier; |
133 |
< |
final Thread[] threads; |
134 |
< |
final boolean[] doneMating; |
135 |
< |
final ReentrantLock matingBarrierLock; |
136 |
< |
final Condition matingBarrier; |
137 |
< |
final LoopHelpers.SimpleRandom[] rngs; |
237 |
> |
final Worker[] threads; |
238 |
> |
final Subpop[] subpops; |
239 |
> |
final Exchanger<Strand> exchanger; |
240 |
> |
final CountDownLatch done; |
241 |
> |
final int nGen; |
242 |
> |
final int subpopSize; |
243 |
|
final int nThreads; |
139 |
– |
volatile int matingBarrierCount; |
140 |
– |
|
141 |
– |
// action to run between each generation |
142 |
– |
class BarrierAction implements Runnable { |
143 |
– |
public void run() { |
144 |
– |
prepareToBreed(); |
145 |
– |
resetMatingBarrier(); |
146 |
– |
} |
147 |
– |
} |
244 |
|
|
245 |
< |
Population(int nCities, |
150 |
< |
int pSize, |
151 |
< |
int nBreeders, |
152 |
< |
int nThreads, |
153 |
< |
int nGen, |
154 |
< |
CyclicBarrier runBarrier) { |
245 |
> |
Population(int nThreads, int nSubpops, int subpopSize, int nGen) { |
246 |
|
this.nThreads = nThreads; |
247 |
< |
// rngs[nThreads] is for global actions; others are per-thread |
248 |
< |
this.rngs = new LoopHelpers.SimpleRandom[nThreads+1]; |
249 |
< |
for (int i = 0; i < rngs.length; ++i) |
250 |
< |
rngs[i] = new LoopHelpers.SimpleRandom(); |
251 |
< |
this.cities = new CitySet(nCities, rngs[nThreads]); |
252 |
< |
this.individuals = new Chromosome[pSize]; |
253 |
< |
for (int i = 0; i < individuals.length; i++) |
254 |
< |
individuals[i] = new Chromosome(cities, nCities, |
255 |
< |
rngs[nThreads]); |
256 |
< |
this.doneMating = new boolean[nThreads]; |
257 |
< |
this.dyers = new int[nBreeders]; |
258 |
< |
this.breeders = new int[nBreeders]; |
259 |
< |
|
260 |
< |
this.x = new Exchanger(); |
261 |
< |
|
262 |
< |
this.matingBarrierLock = new ReentrantLock(); |
263 |
< |
this.matingBarrier = matingBarrierLock.newCondition(); |
264 |
< |
|
265 |
< |
BarrierAction ba = new BarrierAction(); |
266 |
< |
this.generationBarrier = new CyclicBarrier(nThreads, ba); |
267 |
< |
ba.run(); // prepare for first generation |
268 |
< |
|
269 |
< |
this.threads = new Thread[nThreads]; |
270 |
< |
for (int i = 0; i < nThreads; i++) { |
271 |
< |
Runner r = new Runner(i, this, runBarrier, nGen); |
272 |
< |
threads[i] = new Thread(r); |
273 |
< |
threads[i].start(); |
274 |
< |
} |
275 |
< |
} |
276 |
< |
|
277 |
< |
double averageFitness() { |
278 |
< |
double total = 0; |
279 |
< |
for (int i = 0; i < individuals.length; i++) |
280 |
< |
total += individuals[i].fitness; |
281 |
< |
return total/(double)individuals.length; |
282 |
< |
} |
283 |
< |
|
284 |
< |
double bestFitness() { |
285 |
< |
double best = individuals[0].fitness; |
286 |
< |
for (int i = 0; i < individuals.length; i++) |
287 |
< |
if (individuals[i].fitness < best) |
288 |
< |
best = individuals[i].fitness; |
289 |
< |
return best; |
290 |
< |
} |
291 |
< |
|
292 |
< |
void resetMatingBarrier() { |
293 |
< |
matingBarrierCount = nThreads - 1; |
294 |
< |
} |
295 |
< |
|
296 |
< |
void awaitMatingBarrier(int tid) { |
297 |
< |
doneMating[tid] = true; // heuristically set before lock |
298 |
< |
matingBarrierLock.lock(); |
299 |
< |
try { |
300 |
< |
int m = matingBarrierCount--; |
301 |
< |
if (m < 1) { |
302 |
< |
for (int i = 0; i < doneMating.length; ++i) |
303 |
< |
doneMating[i] = false; |
304 |
< |
Thread.interrupted(); // clear |
305 |
< |
matingBarrier.signalAll(); |
306 |
< |
} else { |
307 |
< |
doneMating[tid] = true; |
308 |
< |
if (m == 1 && nThreads > 2) { |
309 |
< |
for (int j = 0; j < doneMating.length; ++j) { |
310 |
< |
if (!doneMating[j]) { |
311 |
< |
threads[j].interrupt(); |
312 |
< |
break; |
313 |
< |
} |
314 |
< |
} |
315 |
< |
} |
316 |
< |
try { |
317 |
< |
do { |
318 |
< |
matingBarrier.await(); |
319 |
< |
} while (matingBarrierCount >= 0); |
320 |
< |
} catch(InterruptedException ie) {} |
230 |
< |
} |
231 |
< |
} finally { |
232 |
< |
matingBarrierLock.unlock(); |
233 |
< |
} |
247 |
> |
this.nGen = nGen; |
248 |
> |
this.subpopSize = subpopSize; |
249 |
> |
this.exchanger = new Exchanger<Strand>(); |
250 |
> |
this.done = new CountDownLatch(nThreads - 1); |
251 |
> |
|
252 |
> |
this.subpops = new Subpop[nSubpops]; |
253 |
> |
for (int i = 0; i < nSubpops; i++) |
254 |
> |
subpops[i] = new Subpop(this); |
255 |
> |
|
256 |
> |
this.threads = new Worker[nThreads]; |
257 |
> |
int maxExchanges = nGen * nSubpops / nThreads; |
258 |
> |
for (int i = 0; i < nThreads; ++i) { |
259 |
> |
threads[i] = new Worker(this, maxExchanges); |
260 |
> |
} |
261 |
> |
|
262 |
> |
} |
263 |
> |
|
264 |
> |
void start() { |
265 |
> |
for (int i = 0; i < nThreads; ++i) { |
266 |
> |
threads[i].start(); |
267 |
> |
} |
268 |
> |
} |
269 |
> |
|
270 |
> |
/** Stop the tasks */ |
271 |
> |
void shutdown() { |
272 |
> |
for (int i = 0; i < threads.length; ++ i) |
273 |
> |
threads[i].interrupt(); |
274 |
> |
} |
275 |
> |
|
276 |
> |
void threadDone() { |
277 |
> |
done.countDown(); |
278 |
> |
} |
279 |
> |
|
280 |
> |
/** Wait for tasks to complete */ |
281 |
> |
void awaitDone() throws InterruptedException { |
282 |
> |
done.await(); |
283 |
> |
} |
284 |
> |
|
285 |
> |
int totalExchanges() { |
286 |
> |
int xs = 0; |
287 |
> |
for (int i = 0; i < threads.length; ++i) |
288 |
> |
xs += threads[i].exchanges; |
289 |
> |
return xs; |
290 |
> |
} |
291 |
> |
|
292 |
> |
/** |
293 |
> |
* Prints statistics, including best and worst tour lengths |
294 |
> |
* for points scaled in [0,1), scaled by the square root of |
295 |
> |
* number of points. This simplifies checking results. The |
296 |
> |
* expected optimal TSP for random points is believed to be |
297 |
> |
* around 0.76 * sqrt(N). For papers discussing this, see |
298 |
> |
* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html |
299 |
> |
*/ |
300 |
> |
void printSnapshot(double secs) { |
301 |
> |
int xs = totalExchanges(); |
302 |
> |
long rate = (xs == 0) ? 0L : (long) ((secs * 1000000000.0) / xs); |
303 |
> |
Chromosome bestc = subpops[0].chromosomes[0]; |
304 |
> |
Chromosome worstc = bestc; |
305 |
> |
for (int k = 0; k < subpops.length; ++k) { |
306 |
> |
Chromosome[] cs = subpops[k].chromosomes; |
307 |
> |
if (cs[0].fitness < bestc.fitness) |
308 |
> |
bestc = cs[0]; |
309 |
> |
int w = cs[cs.length-1].fitness; |
310 |
> |
if (cs[cs.length-1].fitness > worstc.fitness) |
311 |
> |
worstc = cs[cs.length-1]; |
312 |
> |
} |
313 |
> |
double sqrtn = Math.sqrt(cities.length); |
314 |
> |
double best = bestc.unitTourLength() / sqrtn; |
315 |
> |
double worst = worstc.unitTourLength() / sqrtn; |
316 |
> |
System.out.printf("N:%4d T:%8.3f B:%6.3f W:%6.3f X:%9d R:%7d\n", |
317 |
> |
nThreads, secs, best, worst, xs, rate); |
318 |
> |
// exchanger.printStats(); |
319 |
> |
// System.out.print(" s: " + exchanger.aveSpins()); |
320 |
> |
// System.out.print(" p: " + exchanger.aveParks()); |
321 |
|
} |
322 |
+ |
} |
323 |
|
|
324 |
< |
void prepareToBreed() { |
324 |
> |
/** |
325 |
> |
* Worker threads perform updates on subpops. |
326 |
> |
*/ |
327 |
> |
static final class Worker extends Thread { |
328 |
> |
final Population pop; |
329 |
> |
final int maxExchanges; |
330 |
> |
int exchanges; |
331 |
> |
final RNG rng = new RNG(); |
332 |
> |
|
333 |
> |
Worker(Population pop, int maxExchanges) { |
334 |
> |
this.pop = pop; |
335 |
> |
this.maxExchanges = maxExchanges; |
336 |
> |
} |
337 |
|
|
338 |
< |
// Calculate statistics |
339 |
< |
double totalFitness = 0; |
340 |
< |
double worstFitness = 0; |
341 |
< |
double bestFitness = individuals[0].fitness; |
342 |
< |
|
343 |
< |
for (int i = 0; i < individuals.length; i++) { |
344 |
< |
totalFitness += individuals[i].fitness; |
345 |
< |
if (individuals[i].fitness > worstFitness) |
346 |
< |
worstFitness = individuals[i].fitness; |
347 |
< |
if (individuals[i].fitness < bestFitness) |
348 |
< |
bestFitness = individuals[i].fitness; |
349 |
< |
} |
350 |
< |
|
351 |
< |
double[] lifeNorm = new double[individuals.length]; |
352 |
< |
double lifeNormTotal = 0; |
253 |
< |
double[] deathNorm = new double[individuals.length]; |
254 |
< |
double deathNormTotal = 0; |
255 |
< |
for (int i = 0; i < individuals.length; i++) { |
256 |
< |
deathNorm[i] = (individuals[i].fitness - bestFitness) |
257 |
< |
/ (worstFitness - bestFitness + 1) + .05; |
258 |
< |
deathNorm[i] = (deathNorm[i] * deathNorm[i]); |
259 |
< |
lifeNorm[i] = 1.0 - deathNorm[i]; |
260 |
< |
lifeNormTotal += lifeNorm[i]; |
261 |
< |
deathNormTotal += deathNorm[i]; |
262 |
< |
} |
263 |
< |
|
264 |
< |
double deathScale = deathNormTotal / (double)0x7FFFFFFF; |
265 |
< |
double lifeScale = lifeNormTotal / (double)0x7FFFFFFF; |
266 |
< |
|
267 |
< |
int nSub = breeders.length / nThreads; |
268 |
< |
LoopHelpers.SimpleRandom random = rngs[nThreads]; |
269 |
< |
|
270 |
< |
// Select breeders (need to be distinct) |
271 |
< |
for (int i = 0; i < nSub; i++) { |
272 |
< |
boolean newBreeder; |
273 |
< |
int lucky; |
274 |
< |
do { |
275 |
< |
newBreeder = true; |
276 |
< |
double choice = lifeScale * (double)random.next(); |
277 |
< |
for (lucky = 0; lucky < individuals.length; lucky++) { |
278 |
< |
choice -= lifeNorm[lucky]; |
279 |
< |
if (choice <= 0) |
280 |
< |
break; |
281 |
< |
} |
282 |
< |
for (int j = 0; j < i; j++) |
283 |
< |
if (breeders[j] == lucky) |
284 |
< |
newBreeder = false; |
285 |
< |
} while (!newBreeder); |
286 |
< |
breeders[i] = lucky; |
287 |
< |
} |
288 |
< |
|
289 |
< |
// Select dead guys (need to be distinct) |
290 |
< |
for (int i = 0; i < nSub; i++) { |
291 |
< |
boolean newDead; |
292 |
< |
int victim; |
293 |
< |
do { |
294 |
< |
newDead = true; |
295 |
< |
double choice = deathScale * (double)random.next(); |
296 |
< |
for (victim = 0; victim < individuals.length; victim++) { |
297 |
< |
choice -= deathNorm[victim]; |
298 |
< |
if (choice <= 0) |
299 |
< |
break; |
338 |
> |
/** |
339 |
> |
* Repeatedly, find a subpop that is not being updated by |
340 |
> |
* another thread, and run a random number of updates on it. |
341 |
> |
*/ |
342 |
> |
public void run() { |
343 |
> |
try { |
344 |
> |
int len = pop.subpops.length; |
345 |
> |
int pos = (rng.next() & 0x7FFFFFFF) % len; |
346 |
> |
while (exchanges < maxExchanges) { |
347 |
> |
Subpop s = pop.subpops[pos]; |
348 |
> |
AtomicBoolean busy = s.busy; |
349 |
> |
if (!busy.get() && busy.compareAndSet(false, true)) { |
350 |
> |
exchanges += s.runUpdates(); |
351 |
> |
busy.set(false); |
352 |
> |
pos = (rng.next() & 0x7FFFFFFF) % len; |
353 |
|
} |
354 |
< |
for (int j = 0; j < i; j++) |
355 |
< |
if (dyers[j] == victim) |
356 |
< |
newDead = false; |
357 |
< |
} while (!newDead); |
358 |
< |
dyers[i] = victim; |
354 |
> |
else if (++pos >= len) |
355 |
> |
pos = 0; |
356 |
> |
} |
357 |
> |
pop.threadDone(); |
358 |
> |
} catch (InterruptedException fallthrough) { |
359 |
|
} |
307 |
– |
|
360 |
|
} |
361 |
+ |
} |
362 |
|
|
363 |
< |
|
364 |
< |
void nextGeneration(int tid, int matings) |
365 |
< |
throws InterruptedException, BrokenBarrierException { |
366 |
< |
|
367 |
< |
int firstChild = ((individuals.length * tid) / nThreads); |
368 |
< |
int lastChild = ((individuals.length * (tid + 1)) / nThreads); |
369 |
< |
int nChildren = lastChild - firstChild; |
370 |
< |
|
371 |
< |
int firstSub = ((breeders.length * tid) / nThreads); |
372 |
< |
int lastSub = ((breeders.length * (tid + 1)) / nThreads); |
373 |
< |
int nSub = lastSub - firstSub; |
374 |
< |
|
375 |
< |
Chromosome[] children = new Chromosome[nChildren]; |
376 |
< |
|
377 |
< |
LoopHelpers.SimpleRandom random = rngs[tid]; |
378 |
< |
|
379 |
< |
for (int i = 0; i < nSub; i++) { |
380 |
< |
Chromosome parent = individuals[breeders[firstSub + i]]; |
381 |
< |
Chromosome offspring = new Chromosome(parent); |
382 |
< |
int k = 0; |
383 |
< |
while (k < matings && matingBarrierCount > 0) { |
384 |
< |
try { |
385 |
< |
Chromosome other = x.exchange(offspring); |
386 |
< |
offspring = offspring.reproduceWith(other, random); |
387 |
< |
++k; |
388 |
< |
} catch (InterruptedException to) { |
389 |
< |
break; |
390 |
< |
} |
363 |
> |
/** |
364 |
> |
* A Subpop maintains a set of chromosomes. |
365 |
> |
*/ |
366 |
> |
static final class Subpop { |
367 |
> |
/** The chromosomes, kept in sorted order */ |
368 |
> |
final Chromosome[] chromosomes; |
369 |
> |
/** The parent population */ |
370 |
> |
final Population pop; |
371 |
> |
/** Reservation bit for worker threads */ |
372 |
> |
final AtomicBoolean busy; |
373 |
> |
/** The common exchanger, same for all subpops */ |
374 |
> |
final Exchanger<Strand> exchanger; |
375 |
> |
/** The current strand being exchanged */ |
376 |
> |
Strand strand; |
377 |
> |
/** Bitset used in cross */ |
378 |
> |
final int[] inTour; |
379 |
> |
final RNG rng; |
380 |
> |
final int subpopSize; |
381 |
> |
|
382 |
> |
Subpop(Population pop) { |
383 |
> |
this.pop = pop; |
384 |
> |
this.subpopSize = pop.subpopSize; |
385 |
> |
this.exchanger = pop.exchanger; |
386 |
> |
this.busy = new AtomicBoolean(false); |
387 |
> |
this.rng = new RNG(); |
388 |
> |
int length = cities.length; |
389 |
> |
this.strand = new Strand(length); |
390 |
> |
this.inTour = new int[(length >>> 5) + 1]; |
391 |
> |
this.chromosomes = new Chromosome[subpopSize]; |
392 |
> |
for (int j = 0; j < subpopSize; ++j) |
393 |
> |
chromosomes[j] = new Chromosome(length, rng); |
394 |
> |
Arrays.sort(chromosomes); |
395 |
> |
} |
396 |
> |
|
397 |
> |
/** |
398 |
> |
* Run a random number of updates. The number of updates is |
399 |
> |
* at least 1 and no more than subpopSize. This |
400 |
> |
* controls the granularity of multiplexing subpop updates on |
401 |
> |
* to threads. It is small enough to balance out updates |
402 |
> |
* across tasks, but large enough to avoid having runs |
403 |
> |
* dominated by subpop selection. It is randomized to avoid |
404 |
> |
* long runs where pairs of subpops exchange only with each |
405 |
> |
* other. It is hardwired because small variations of it |
406 |
> |
* don't matter much. |
407 |
> |
* |
408 |
> |
* @param g the first generation to run |
409 |
> |
*/ |
410 |
> |
int runUpdates() throws InterruptedException { |
411 |
> |
int n = 1 + (rng.next() & ((subpopSize << 1) - 1)); |
412 |
> |
for (int i = 0; i < n; ++i) |
413 |
> |
update(); |
414 |
> |
return n; |
415 |
> |
} |
416 |
> |
|
417 |
> |
/** |
418 |
> |
* Chooses a breeder, exchanges strand with another subpop, and |
419 |
> |
* crosses them to create new chromosome to replace a chosen |
420 |
> |
* dyer. |
421 |
> |
*/ |
422 |
> |
void update() throws InterruptedException { |
423 |
> |
int b = chooseBreeder(); |
424 |
> |
int d = chooseDyer(b); |
425 |
> |
Chromosome breeder = chromosomes[b]; |
426 |
> |
Chromosome child = chromosomes[d]; |
427 |
> |
chooseStrand(breeder); |
428 |
> |
strand = exchanger.exchange(strand); |
429 |
> |
cross(breeder, child); |
430 |
> |
fixOrder(child, d); |
431 |
> |
} |
432 |
> |
|
433 |
> |
/** |
434 |
> |
* Chooses a breeder, with exponentially decreasing probability |
435 |
> |
* starting at best. |
436 |
> |
* @return index of selected breeder |
437 |
> |
*/ |
438 |
> |
int chooseBreeder() { |
439 |
> |
int mask = (subpopSize >>> BREEDER_DECAY) - 1; |
440 |
> |
int b = 0; |
441 |
> |
while ((rng.next() & mask) != mask) { |
442 |
> |
if (++b >= subpopSize) |
443 |
> |
b = 0; |
444 |
> |
} |
445 |
> |
return b; |
446 |
> |
} |
447 |
> |
|
448 |
> |
/** |
449 |
> |
* Chooses a chromosome that will be replaced, with |
450 |
> |
* exponentially decreasing probability starting at |
451 |
> |
* worst, ignoring the excluded index. |
452 |
> |
* @param exclude index to ignore; use -1 to not exclude any |
453 |
> |
* @return index of selected dyer |
454 |
> |
*/ |
455 |
> |
int chooseDyer(int exclude) { |
456 |
> |
int mask = (subpopSize >>> DYER_DECAY) - 1; |
457 |
> |
int d = subpopSize - 1; |
458 |
> |
while (d == exclude || (rng.next() & mask) != mask) { |
459 |
> |
if (--d < 0) |
460 |
> |
d = subpopSize - 1; |
461 |
> |
} |
462 |
> |
return d; |
463 |
> |
} |
464 |
> |
|
465 |
> |
/** |
466 |
> |
* Select a random strand of b's. |
467 |
> |
* @param breeder the breeder |
468 |
> |
*/ |
469 |
> |
void chooseStrand(Chromosome breeder) { |
470 |
> |
int[] bs = breeder.alleles; |
471 |
> |
int length = bs.length; |
472 |
> |
int strandLength = MIN_STRAND_LENGTH; |
473 |
> |
while (strandLength < length && |
474 |
> |
(rng.next() & RANDOM_STRAND_MASK) != RANDOM_STRAND_MASK) |
475 |
> |
strandLength++; |
476 |
> |
strand.strandLength = strandLength; |
477 |
> |
int[] ss = strand.alleles; |
478 |
> |
int k = (rng.next() & 0x7FFFFFFF) % length; |
479 |
> |
for (int i = 0; i < strandLength; ++i) { |
480 |
> |
ss[i] = bs[k]; |
481 |
> |
if (++k >= length) k = 0; |
482 |
> |
} |
483 |
> |
} |
484 |
> |
|
485 |
> |
/** |
486 |
> |
* Copies current strand to start of c's, and then appends all |
487 |
> |
* remaining b's that aren't in the strand. |
488 |
> |
* @param breeder the breeder |
489 |
> |
* @param child the child |
490 |
> |
*/ |
491 |
> |
void cross(Chromosome breeder, Chromosome child) { |
492 |
> |
for (int k = 0; k < inTour.length; ++k) // clear bitset |
493 |
> |
inTour[k] = 0; |
494 |
> |
|
495 |
> |
// Copy current strand to c |
496 |
> |
int[] cs = child.alleles; |
497 |
> |
int ssize = strand.strandLength; |
498 |
> |
int[] ss = strand.alleles; |
499 |
> |
int i; |
500 |
> |
for (i = 0; i < ssize; ++i) { |
501 |
> |
int x = ss[i]; |
502 |
> |
cs[i] = x; |
503 |
> |
inTour[x >>> 5] |= 1 << (x & 31); // record in bit set |
504 |
> |
} |
505 |
> |
|
506 |
> |
// Find index of matching origin in b |
507 |
> |
int first = cs[0]; |
508 |
> |
int j = 0; |
509 |
> |
int[] bs = breeder.alleles; |
510 |
> |
while (bs[j] != first) |
511 |
> |
++j; |
512 |
> |
|
513 |
> |
// Append remaining b's that aren't already in tour |
514 |
> |
while (i < cs.length) { |
515 |
> |
if (++j >= bs.length) j = 0; |
516 |
> |
int x = bs[j]; |
517 |
> |
if ((inTour[x >>> 5] & (1 << (x & 31))) == 0) |
518 |
> |
cs[i++] = x; |
519 |
> |
} |
520 |
> |
|
521 |
> |
} |
522 |
> |
|
523 |
> |
/** |
524 |
> |
* Fixes the sort order of a changed Chromosome c at position k. |
525 |
> |
* @param c the chromosome |
526 |
> |
* @param k the index |
527 |
> |
*/ |
528 |
> |
void fixOrder(Chromosome c, int k) { |
529 |
> |
Chromosome[] cs = chromosomes; |
530 |
> |
int oldFitness = c.fitness; |
531 |
> |
c.recalcFitness(); |
532 |
> |
int newFitness = c.fitness; |
533 |
> |
if (newFitness < oldFitness) { |
534 |
> |
int j = k; |
535 |
> |
int p = j - 1; |
536 |
> |
while (p >= 0 && cs[p].fitness > newFitness) { |
537 |
> |
cs[j] = cs[p]; |
538 |
> |
j = p--; |
539 |
|
} |
540 |
< |
if (k != 0) |
541 |
< |
children[i] = offspring; |
542 |
< |
else { |
543 |
< |
// No peers, so we mate with ourselves |
544 |
< |
for ( ; i < nSub - 1; i += 2) { |
545 |
< |
int cur = firstSub + i; |
546 |
< |
Chromosome bro = individuals[breeders[cur]]; |
346 |
< |
Chromosome sis = individuals[breeders[cur + 1]]; |
347 |
< |
|
348 |
< |
children[i] = bro.breedWith(sis, matings, random); |
349 |
< |
children[i+1] = sis.breedWith(bro, matings, random); |
350 |
< |
} |
351 |
< |
|
352 |
< |
// Not even a sibling, so we go to asexual reproduction |
353 |
< |
if (i < nSub) |
354 |
< |
children[i] = individuals[breeders[firstSub + 1]]; |
355 |
< |
break; |
540 |
> |
cs[j] = c; |
541 |
> |
} else if (newFitness > oldFitness) { |
542 |
> |
int j = k; |
543 |
> |
int n = j + 1; |
544 |
> |
while (n < cs.length && cs[n].fitness < newFitness) { |
545 |
> |
cs[j] = cs[n]; |
546 |
> |
j = n++; |
547 |
|
} |
548 |
< |
|
548 |
> |
cs[j] = c; |
549 |
|
} |
359 |
– |
|
360 |
– |
awaitMatingBarrier(tid); |
361 |
– |
|
362 |
– |
// Kill off dead guys |
363 |
– |
for (int i = 0; i < nSub; i++) { |
364 |
– |
individuals[dyers[firstSub + 1]] = children[i]; |
365 |
– |
} |
366 |
– |
|
367 |
– |
generationBarrier.await(); |
550 |
|
} |
551 |
|
} |
552 |
|
|
553 |
< |
static final class Chromosome { |
554 |
< |
private final CitySet cities; |
555 |
< |
private final int[] alleles; |
556 |
< |
private final int length; |
557 |
< |
public double fitness; // immutable after publication |
558 |
< |
|
559 |
< |
// Basic constructor - gets randomized genetic code |
560 |
< |
Chromosome(CitySet cities, int length, |
561 |
< |
LoopHelpers.SimpleRandom random) { |
562 |
< |
this.length = length; |
563 |
< |
this.cities = cities; |
564 |
< |
// Initialize alleles to a random shuffle |
553 |
> |
/** |
554 |
> |
* A Chromosome is a candidate TSP tour. |
555 |
> |
*/ |
556 |
> |
static final class Chromosome implements Comparable { |
557 |
> |
/** Index of cities in tour order */ |
558 |
> |
final int[] alleles; |
559 |
> |
/** Total tour length */ |
560 |
> |
int fitness; |
561 |
> |
|
562 |
> |
/** |
563 |
> |
* Initializes to random tour. |
564 |
> |
*/ |
565 |
> |
Chromosome(int length, RNG random) { |
566 |
|
alleles = new int[length]; |
567 |
|
for (int i = 0; i < length; i++) |
568 |
|
alleles[i] = i; |
569 |
|
for (int i = length - 1; i > 0; i--) { |
570 |
+ |
int idx = (random.next() & 0x7FFFFFFF) % alleles.length; |
571 |
|
int tmp = alleles[i]; |
388 |
– |
int idx = random.next() % length; |
572 |
|
alleles[i] = alleles[idx]; |
573 |
|
alleles[idx] = tmp; |
574 |
|
} |
575 |
|
recalcFitness(); |
576 |
|
} |
577 |
< |
|
578 |
< |
// Copy constructor - clones parent's genetic code |
579 |
< |
Chromosome(Chromosome clone) { |
580 |
< |
length = clone.length; |
581 |
< |
cities = clone.cities; |
399 |
< |
fitness = clone.fitness; |
400 |
< |
alleles = new int[length]; |
401 |
< |
System.arraycopy(clone.alleles, 0, alleles, 0, length); |
402 |
< |
} |
403 |
< |
|
404 |
< |
int getAllele(int offset) { |
405 |
< |
return alleles[offset % length]; |
406 |
< |
} |
407 |
< |
void setAllele(int offset, int v) { |
408 |
< |
alleles[offset % length] = v; |
577 |
> |
|
578 |
> |
public int compareTo(Object x) { // to enable sorting |
579 |
> |
int xf = ((Chromosome) x).fitness; |
580 |
> |
int f = fitness; |
581 |
> |
return ((f == xf) ? 0 :((f < xf) ? -1 : 1)); |
582 |
|
} |
583 |
< |
|
583 |
> |
|
584 |
|
void recalcFitness() { |
585 |
< |
fitness = cities.distanceBetween(alleles[0], alleles[length-1]); |
586 |
< |
for (int i = 1; i < length; i++) { |
587 |
< |
fitness += cities.distanceBetween(alleles[i-1], alleles[i]); |
588 |
< |
} |
589 |
< |
} |
590 |
< |
|
591 |
< |
Chromosome breedWith(Chromosome partner, int n, |
592 |
< |
LoopHelpers.SimpleRandom random) { |
593 |
< |
Chromosome offspring = new Chromosome(this); |
594 |
< |
for (int i = 0; i < n; i++) |
595 |
< |
offspring = offspring.reproduceWith(partner, random); |
596 |
< |
return offspring; |
597 |
< |
} |
598 |
< |
|
599 |
< |
Chromosome reproduceWith(Chromosome other, |
600 |
< |
LoopHelpers.SimpleRandom random) { |
601 |
< |
Chromosome child = new Chromosome(this); |
602 |
< |
int coStart = random.next() % length; |
603 |
< |
int coLen = 3; |
604 |
< |
while (1 == (random.next() & 1) && (coLen < length)) |
605 |
< |
coLen++; |
606 |
< |
int cPos, pPos; |
607 |
< |
|
608 |
< |
int join = other.getAllele(coStart); |
609 |
< |
child.alleles[0] = join; |
610 |
< |
|
611 |
< |
for (pPos = 0; alleles[pPos] != join; pPos++) |
612 |
< |
; |
613 |
< |
|
614 |
< |
for (cPos = 1; cPos < coLen; cPos++) |
615 |
< |
child.setAllele(cPos, other.getAllele(coStart + cPos)); |
616 |
< |
|
617 |
< |
for (int i = 0; i < length; i++) { |
618 |
< |
boolean found = false; |
619 |
< |
int allele = getAllele(pPos++); |
620 |
< |
for (int j = 0; j < coLen; j++) { |
621 |
< |
if (found = (child.getAllele(j) == allele)) |
622 |
< |
break; |
623 |
< |
} |
451 |
< |
if (!found) |
452 |
< |
child.setAllele(cPos++, allele); |
453 |
< |
} |
454 |
< |
|
455 |
< |
child.recalcFitness(); |
456 |
< |
return child; |
585 |
> |
int[] a = alleles; |
586 |
> |
int len = a.length; |
587 |
> |
int p = a[0]; |
588 |
> |
long f = cities.distanceBetween(a[len-1], p); |
589 |
> |
for (int i = 1; i < len; i++) { |
590 |
> |
int n = a[i]; |
591 |
> |
f += cities.distanceBetween(p, n); |
592 |
> |
p = n; |
593 |
> |
} |
594 |
> |
fitness = (int) (f / len); |
595 |
> |
} |
596 |
> |
|
597 |
> |
/** |
598 |
> |
* Returns tour length for points scaled in [0, 1). |
599 |
> |
*/ |
600 |
> |
double unitTourLength() { |
601 |
> |
int[] a = alleles; |
602 |
> |
int len = a.length; |
603 |
> |
int p = a[0]; |
604 |
> |
double f = cities.unitDistanceBetween(a[len-1], p); |
605 |
> |
for (int i = 1; i < len; i++) { |
606 |
> |
int n = a[i]; |
607 |
> |
f += cities.unitDistanceBetween(p, n); |
608 |
> |
p = n; |
609 |
> |
} |
610 |
> |
return f; |
611 |
> |
} |
612 |
> |
|
613 |
> |
/** |
614 |
> |
* Checks that this tour visits each city. |
615 |
> |
*/ |
616 |
> |
void validate() { |
617 |
> |
int len = alleles.length; |
618 |
> |
boolean[] used = new boolean[len]; |
619 |
> |
for (int i = 0; i < len; ++i) |
620 |
> |
used[alleles[i]] = true; |
621 |
> |
for (int i = 0; i < len; ++i) |
622 |
> |
if (!used[i]) |
623 |
> |
throw new Error("Bad tour"); |
624 |
|
} |
625 |
< |
|
625 |
> |
|
626 |
|
} |
627 |
+ |
|
628 |
|
/** |
629 |
< |
* A collection of (x,y) points that represent cities |
629 |
> |
* A Strand is a random sub-sequence of a Chromosome. Each subpop |
630 |
> |
* creates only one strand, and then trades it with others, |
631 |
> |
* refilling it on each iteration. |
632 |
> |
*/ |
633 |
> |
static final class Strand { |
634 |
> |
final int[] alleles; |
635 |
> |
int strandLength; |
636 |
> |
Strand(int length) { alleles = new int[length]; } |
637 |
> |
} |
638 |
> |
|
639 |
> |
/** |
640 |
> |
* A collection of (x,y) points that represent cities. |
641 |
|
*/ |
642 |
|
static final class CitySet { |
643 |
< |
final int XMAX = 1000; |
465 |
< |
final int YMAX = 1000; |
643 |
> |
|
644 |
|
final int length; |
645 |
< |
final int xPts[]; |
646 |
< |
final int yPts[]; |
647 |
< |
final double distances[]; |
648 |
< |
|
649 |
< |
CitySet(int n, LoopHelpers.SimpleRandom random) { |
645 |
> |
final int[] xPts; |
646 |
> |
final int[] yPts; |
647 |
> |
final int[][] distances; |
648 |
> |
|
649 |
> |
CitySet(int n) { |
650 |
|
this.length = n; |
651 |
< |
xPts = new int[n]; |
652 |
< |
yPts = new int [n]; |
651 |
> |
this.xPts = new int[n]; |
652 |
> |
this.yPts = new int[n]; |
653 |
> |
this.distances = new int[n][n]; |
654 |
> |
|
655 |
> |
RNG random = new RNG(); |
656 |
|
for (int i = 0; i < n; i++) { |
657 |
< |
xPts[i] = random.next() % XMAX; |
658 |
< |
yPts[i] = random.next() % YMAX; |
657 |
> |
xPts[i] = (random.next() & 0x7FFFFFFF); |
658 |
> |
yPts[i] = (random.next() & 0x7FFFFFFF); |
659 |
|
} |
660 |
< |
distances = new double[n * n]; |
660 |
> |
|
661 |
|
for (int i = 0; i < n; i++) { |
662 |
|
for (int j = 0; j < n; j++) { |
663 |
< |
double dX = (double)(xPts[i] - xPts[j]); |
664 |
< |
double dY = (double)(yPts[i] - yPts[j]); |
665 |
< |
distances[i + j * n] = Math.hypot(dX, dY); |
663 |
> |
double dx = (double) xPts[i] - (double) xPts[j]; |
664 |
> |
double dy = (double) yPts[i] - (double) yPts[j]; |
665 |
> |
double dd = Math.hypot(dx, dy) / 2.0; |
666 |
> |
long ld = Math.round(dd); |
667 |
> |
distances[i][j] = (ld >= Integer.MAX_VALUE) ? |
668 |
> |
Integer.MAX_VALUE : (int) ld; |
669 |
|
} |
670 |
|
} |
671 |
|
} |
672 |
< |
|
673 |
< |
// Retrieve the cached distance between a pair of cities |
674 |
< |
double distanceBetween(int idx1, int idx2) { |
675 |
< |
return distances[idx1 + idx2 * length]; |
672 |
> |
|
673 |
> |
/** |
674 |
> |
* Returns the cached distance between a pair of cities. |
675 |
> |
*/ |
676 |
> |
int distanceBetween(int i, int j) { |
677 |
> |
return distances[i][j]; |
678 |
> |
} |
679 |
> |
|
680 |
> |
// Scale ints to doubles in [0,1) |
681 |
> |
static final double PSCALE = (double) 0x80000000L; |
682 |
> |
|
683 |
> |
/** |
684 |
> |
* Returns distance for points scaled in [0,1). This simplifies |
685 |
> |
* checking results. The expected optimal TSP for random |
686 |
> |
* points is believed to be around 0.76 * sqrt(N). For papers |
687 |
> |
* discussing this, see |
688 |
> |
* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html |
689 |
> |
*/ |
690 |
> |
double unitDistanceBetween(int i, int j) { |
691 |
> |
double dx = ((double) xPts[i] - (double) xPts[j]) / PSCALE; |
692 |
> |
double dy = ((double) yPts[i] - (double) yPts[j]) / PSCALE; |
693 |
> |
return Math.hypot(dx, dy); |
694 |
|
} |
695 |
+ |
|
696 |
|
} |
697 |
|
|
698 |
< |
static final class Runner implements Runnable { |
699 |
< |
final Population pop; |
700 |
< |
final CyclicBarrier b; |
701 |
< |
final int nGen; |
702 |
< |
final int tid; |
703 |
< |
static final boolean verbose = false; |
704 |
< |
|
705 |
< |
Runner(int tid, Population p, CyclicBarrier b, int n) { |
706 |
< |
this.tid = tid; |
707 |
< |
this.pop = p; |
708 |
< |
this.b = b; |
709 |
< |
this.nGen = n; |
698 |
> |
/** |
699 |
> |
* Cheap XorShift random number generator |
700 |
> |
*/ |
701 |
> |
static final class RNG { |
702 |
> |
/** Seed generator for XorShift RNGs */ |
703 |
> |
static final Random seedGenerator = new Random(); |
704 |
> |
|
705 |
> |
int seed; |
706 |
> |
RNG(int seed) { this.seed = seed; } |
707 |
> |
RNG() { this.seed = seedGenerator.nextInt() | 1; } |
708 |
> |
|
709 |
> |
int next() { |
710 |
> |
int x = seed; |
711 |
> |
x ^= x << 6; |
712 |
> |
x ^= x >>> 21; |
713 |
> |
x ^= x << 7; |
714 |
> |
seed = x; |
715 |
> |
return x; |
716 |
|
} |
717 |
< |
|
717 |
> |
} |
718 |
> |
|
719 |
> |
static final class ProgressMonitor extends Thread { |
720 |
> |
final Population pop; |
721 |
> |
ProgressMonitor(Population p) { pop = p; } |
722 |
|
public void run() { |
723 |
+ |
double time = 0; |
724 |
|
try { |
725 |
< |
b.await(); |
726 |
< |
for (int i = 0; i < nGen; i++) { |
727 |
< |
if (verbose && 0 == tid && 0 == i % 1000) { |
728 |
< |
System.out.print("Gen " + i + " fitness:"); |
515 |
< |
System.out.print(" best=" + (int)pop.bestFitness()); |
516 |
< |
System.out.println("; avg=" + (int)pop.averageFitness()); |
517 |
< |
} |
518 |
< |
int matings = (((nGen - i) * 2) / (nGen)) + 1; |
519 |
< |
pop.nextGeneration(tid, matings); |
725 |
> |
while (!Thread.interrupted()) { |
726 |
> |
sleep(SNAPSHOT_RATE); |
727 |
> |
time += SNAPSHOT_RATE; |
728 |
> |
pop.printSnapshot(time / 1000.0); |
729 |
|
} |
730 |
< |
b.await(); |
522 |
< |
} |
523 |
< |
catch (InterruptedException e) { |
524 |
< |
e.printStackTrace(System.out); |
525 |
< |
System.exit(0); |
526 |
< |
} |
527 |
< |
catch (BrokenBarrierException e) { |
528 |
< |
e.printStackTrace(System.out); |
529 |
< |
System.exit(0); |
530 |
< |
} |
730 |
> |
} catch (InterruptedException ie) {} |
731 |
|
} |
732 |
|
} |
733 |
|
} |