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minor changes due to new API
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@thomasWeise
thomasWeise committed Oct 4, 2020
1 parent 8650172 commit 5e7d74c
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Showing 2 changed files with 93 additions and 102 deletions.
161 changes: 85 additions & 76 deletions src/main/java/aitoa/examples/jssp/EJSSPExperimentStage.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -50,11 +50,12 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>>
getAlgorithms(//
final JSSPMakespanObjectiveFunction problem) {
final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);

return Stream.of(//
() -> new SingleRandomSample<>(nullary), //
() -> new RandomSampling<>(nullary));
() -> new SingleRandomSample<>(
new JSSPNullaryOperator(problem.instance)), //
() -> new RandomSampling<>(
new JSSPNullaryOperator(problem.instance)));
}
},

Expand All @@ -81,18 +82,18 @@ public enum EJSSPExperimentStage implements
final ArrayList<Supplier<
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();
final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);

for (final IUnarySearchOperator<
int[]> unary : new IUnarySearchOperator[] {
new JSSPUnaryOperator1Swap(),
new JSSPUnaryOperatorNSwap() }) {
list.add(() -> new HillClimber<>(nullary, unary));
list.add(() -> new HillClimber<>(
new JSSPNullaryOperator(problem.instance), unary));
for (int i = 7; i <= 18; i++) {
final int pow = 1 << i;
list.add(() -> new HillClimberWithRestarts<>(nullary,
unary, pow));
list.add(() -> new HillClimberWithRestarts<>(
new JSSPNullaryOperator(problem.instance), unary,
pow));
}
}

Expand Down Expand Up @@ -122,24 +123,24 @@ public enum EJSSPExperimentStage implements
final ArrayList<Supplier<
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();
final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
final JSSPBinaryOperatorSequence binary =
new JSSPBinaryOperatorSequence(problem.instance);
final JSSPUnaryOperator1Swap unary =
new JSSPUnaryOperator1Swap();

for (final double cr : new double[] { 0, 0.05, 0.3 }) {

for (final int mu : new int[] { 128, 256, 512, 1024,
2048, 4096, 8192, 16384, 32768, 65536 }) {
list.add(() -> new EA<>(nullary, unary, binary, cr, mu,
mu));
list.add(() -> new EA<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap(),
new JSSPBinaryOperatorSequence(problem.instance),
cr, mu, mu));
}

for (final int mu : new int[] { 128, 256, 512, 1024, }) {
list.add(() -> new EAWithClearing<>(nullary, unary,
binary, cr, mu, mu));
list.add(() -> new EAWithClearing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap(),
new JSSPBinaryOperatorSequence(problem.instance),
cr, mu, mu));
}
}

Expand Down Expand Up @@ -171,29 +172,28 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();

final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
final JSSPUnaryOperatorNSwap unary =
new JSSPUnaryOperatorNSwap();
final JSSPBinaryOperatorSequence binary =
new JSSPBinaryOperatorSequence(problem.instance);

for (final double cr : new double[] { 0d, 0.05d, 0.3d,
0.98d }) {

for (final int mu : new int[] { 128, 256, 512, 1024,
2048, 4096, 8192, 16384, 16384, 32768, 65536 }) {
list.add(() -> new EA<>(nullary, unary, binary, cr, mu,
mu));
list.add(() -> new EA<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperatorNSwap(),
new JSSPBinaryOperatorSequence(problem.instance),
cr, mu, mu));
}
}

for (final double cr : new double[] { 0.05 }) {
for (final int mu : new int[] { 4, 8, 16, 32, 64, 128,
256, 512, 1024, 2048, 4096, 8192, 16384, 16384,
32768, 65536 }) {
list.add(() -> new EAWithClearing<>(nullary, unary,
binary, cr, mu, mu));
list.add(() -> new EAWithClearing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperatorNSwap(),
new JSSPBinaryOperatorSequence(problem.instance),
cr, mu, mu));
}
}

Expand Down Expand Up @@ -225,14 +225,11 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();

final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
final JSSPUnaryOperator1Swap unary =
new JSSPUnaryOperator1Swap();

for (final double Ts : new double[] { 20d, 0.5d * 20d,
0.25d * 20d }) {
list.add(() -> new SimulatedAnnealing<>(nullary, unary,
list.add(() -> new SimulatedAnnealing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap(),
new TemperatureSchedule.Logarithmic(Ts, 1)));
}

Expand All @@ -244,9 +241,12 @@ public enum EJSSPExperimentStage implements
2e-7d, //
4e-7d, //
8e-7d }) {
list.add(() -> new SimulatedAnnealing<>(nullary, unary,
list.add(() -> new SimulatedAnnealing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap(),
new TemperatureSchedule.Exponential(20d, ep)));
} // end epsilon
} // end
// epsilon

return list.stream();
}
Expand All @@ -265,7 +265,6 @@ public enum EJSSPExperimentStage implements
* the problem
* @return the stream of suppliers
*/
@SuppressWarnings("unchecked")
@Override
public
Stream<Supplier<
Expand All @@ -276,18 +275,27 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();

final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
list.add(() -> new HillClimber2<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap()));
list.add(() -> new HillClimber2WithRestarts<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1Swap()));

list.add(() -> new HillClimber2<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance)));
list.add(() -> new HillClimber2WithRestarts<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance)));

list.add(() -> new HillClimber2<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator12Swap()));
list.add(() -> new HillClimber2WithRestarts<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator12Swap()));

for (final IUnarySearchOperator<
int[]> unary : new IUnarySearchOperator[] {
new JSSPUnaryOperator1Swap(),
new JSSPUnaryOperator1SwapU(problem.instance),
new JSSPUnaryOperator12Swap() }) {
list.add(() -> new HillClimber2<>(nullary, unary));
list.add(() -> new HillClimber2WithRestarts<>(nullary,
unary));
}
return list.stream();
}
},
Expand Down Expand Up @@ -316,20 +324,19 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();

final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
final JSSPUnaryOperator1SwapU unary =
new JSSPUnaryOperator1SwapU(problem.instance);
final JSSPBinaryOperatorSequence binary =
new JSSPBinaryOperatorSequence(problem.instance);

for (final int mu : new int[] { 8, 16 }) {
for (final int ls : new int[] { Integer.MAX_VALUE,
32 }) {
list.add(() -> new MA<>(nullary, unary, binary, mu, mu,
ls));
list.add(() -> new MAWithClearing<>(nullary, unary,
binary, mu, mu, ls));
list.add(() -> new MA<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance),
new JSSPBinaryOperatorSequence(problem.instance),
mu, mu, ls));
list.add(() -> new MAWithClearing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance),
new JSSPBinaryOperatorSequence(problem.instance),
mu, mu, ls));
}
}
return list.stream();
Expand Down Expand Up @@ -359,25 +366,27 @@ public enum EJSSPExperimentStage implements
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();

final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);

for (final int mu : new int[] { 2, 3, 4, 10, 1000 }) {
list.add(() -> new EDAWithClearing<>(nullary, mu, 32768, //
list.add(() -> new EDAWithClearing<>(
new JSSPNullaryOperator(problem.instance), mu, 32768, //
new JSSPUMDAModel(problem.instance)));
list.add(() -> new EDAWithClearing<>(nullary, mu, 4096, //
list.add(() -> new EDAWithClearing<>(
new JSSPNullaryOperator(problem.instance), mu, 4096, //
new JSSPUMDAModel(problem.instance)));

for (int lambdaShift = 4; lambdaShift <= 18;
lambdaShift++) {
final int lambda = 1 << lambdaShift;
if (mu < lambda) {
if (lambda <= 256) {
list.add(() -> new EDAWithClearing<>(nullary, mu,
list.add(() -> new EDAWithClearing<>(
new JSSPNullaryOperator(problem.instance), mu,
lambda, //
new JSSPUMDAModel(problem.instance)));
}
list.add(() -> new EDA<>(nullary, mu, lambda, //
list.add(() -> new EDA<>(
new JSSPNullaryOperator(problem.instance), mu,
lambda, //
new JSSPUMDAModel(problem.instance)));
}
}
Expand Down Expand Up @@ -409,21 +418,21 @@ public enum EJSSPExperimentStage implements
final ArrayList<Supplier<
IMetaheuristic<int[], JSSPCandidateSolution>>> list =
new ArrayList<>();
final JSSPNullaryOperator nullary =
new JSSPNullaryOperator(problem.instance);
final JSSPUnaryOperator1SwapU unary =
new JSSPUnaryOperator1SwapU(problem.instance);

for (int lambdaShift = 2; lambdaShift <= 6;
lambdaShift++) {
final int lambda = 1 << lambdaShift;
for (final int mu : new int[] { 2, 4, 8, 16 }) {
if (mu < lambda) {
list.add(() -> new HybridEDAWithClearing<>(nullary,
unary, mu, lambda, Integer.MAX_VALUE, //
list.add(() -> new HybridEDAWithClearing<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance),
mu, lambda, Integer.MAX_VALUE, //
new JSSPUMDAModel(problem.instance)));
list.add(() -> new HybridEDA<>(nullary, unary, mu,
lambda, Integer.MAX_VALUE, //
list.add(() -> new HybridEDA<>(
new JSSPNullaryOperator(problem.instance),
new JSSPUnaryOperator1SwapU(problem.instance),
mu, lambda, Integer.MAX_VALUE, //
new JSSPUMDAModel(problem.instance)));
}
}
Expand Down
34 changes: 8 additions & 26 deletions src/main/java/aitoa/examples/jssp/EJSSPExperimentStageACO.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -41,36 +41,18 @@ public enum EJSSPExperimentStageACO implements
JSSPACOIndividual>>> list = new ArrayList<>();

for (final int mu : new int[] { 1 }) {
for (final int lambda : new int[] { 512, 1024, 2048 }) {
for (final int k : new int[] { 4, 5, 8, 16 }) {
for (final double beta : new double[] { 1.5d, 2d,
2.5d, 3d }) {
for (final double q0 : new double[] { 0.1d, 0.3d,
0.5d, 0.9d }) {
for (final double tauMax : new double[] { 1d }) {
final JSSPPACOModelAge model =
new JSSPPACOModelAge(problem.getInstance(), //
k, q0, beta, tauMax);
list.add(
() -> new EDA<>(model, mu, lambda, model));
}
}
}
}
}
}

for (final int mu : new int[] { 1 }) {
for (final int lambda : new int[] { 4096, 8192 }) {
for (final int lambda : new int[] { 1024, 2048 }) {
for (final int k : new int[] { 4, 5 }) {
for (final double beta : new double[] { 2.5d }) {
for (final double q0 : new double[] { 0.5d }) {
for (final double tauMax : new double[] { 1d }) {
final JSSPPACOModelAge model =
new JSSPPACOModelAge(problem.getInstance(), //
k, q0, beta, tauMax);
list.add(
() -> new EDA<>(model, mu, lambda, model));
list.add(() -> {
final JSSPPACOModelAge model =
new JSSPPACOModelAge(
problem.getInstance(), //
k, q0, beta, tauMax);
return new EDA<>(model, mu, lambda, model);
});
}
}
}
Expand Down

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