fixed UMDA for JSSP

thomasWeise · Apr 26, 2020 · f27bab2 · f27bab2
1 parent 205890c
commit f27bab2
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Showing 14 changed files with 114 additions and 691 deletions.
diff --git a/README.md b/README.md
@@ -44,15 +44,15 @@ First, you need to add the following repository, which is a repository that can
 ```
 
 Than you can add the dependency on our `aitoa-code` repository into your `dependencies` section.
-Here, `0.8.53` is the current version of  `aitoa-code`.
+Here, `0.8.54` is the current version of  `aitoa-code`.
 Notice that you may have more dependencies in your `dependencies` section, say on `junit`, but here I just put the one for `aitoa-code` as example.
 
 ```xml
 <dependencies>
   <dependency>
     <groupId>com.github.thomasWeise</groupId>
     <artifactId>aitoa-code</artifactId>
-    <version>0.8.53</version>
+    <version>0.8.54</version>
   </dependency>
 </dependencies>
 ```

diff --git a/pom.xml b/pom.xml
@@ -5,7 +5,7 @@
 
 	<groupId>aitoa</groupId>
 	<artifactId>aitoa-code</artifactId>
-	<version>0.8.53</version>
+	<version>0.8.54</version>
 	<packaging>jar</packaging>
 	<name>aitoa-code</name>
 	<description>Example Source Codes from the Book "Introduction to Optimization Algorithms"</description>

diff --git a/src/main/java/aitoa/examples/jssp/EJSSPExperimentStage.java b/src/main/java/aitoa/examples/jssp/EJSSPExperimentStage.java
@@ -14,6 +14,7 @@
 import aitoa.algorithms.HillClimber2;
 import aitoa.algorithms.HillClimber2WithRestarts;
 import aitoa.algorithms.HillClimberWithRestarts;
+import aitoa.algorithms.HybridEDA;
 import aitoa.algorithms.MA;
 import aitoa.algorithms.MAWithClearing;
 import aitoa.algorithms.RandomSampling;
@@ -508,19 +509,68 @@ public void configureBuilderForProblem(
             IMetaheuristic<int[], JSSPCandidateSolution>>>
         getAlgorithms(//
             final JSSPMakespanObjectiveFunction problem) {
+
       final ArrayList<Supplier<
           IMetaheuristic<int[], JSSPCandidateSolution>>> list =
               new ArrayList<>();
-      for (int i = 10; i <= 16; i++) {
-        final int lambda = (1 << i);
-        for (int j = 6; j <= 8; j++) {
-          final int mu = lambda / (1 << j);
-          list.add(() -> new EDA<>(mu, lambda, //
-              new JSSPUMDAModel(problem.instance)));
-          list.add(() -> new EDA<>(mu, lambda, //
-              new JSSPSpreadModel(problem.instance, 16)));
-        }
-      }
+
+      list.add(() -> new EDA<>(1, 1 << 5, //
+          new JSSPUMDAModel(problem.instance, 64L)));
+      list.add(() -> new EDA<>(2, 1 << 5, //
+          new JSSPUMDAModel(problem.instance, 32L)));
+
+      list.add(() -> new EDA<>(1, 1 << 10, //
+          new JSSPUMDAModel(problem.instance, 64L)));
+      list.add(() -> new EDA<>(2, 1 << 10, //
+          new JSSPUMDAModel(problem.instance, 32L)));
+
+      list.add(() -> new EDA<>(1, 1 << 15, //
+          new JSSPUMDAModel(problem.instance, 64L)));
+      list.add(() -> new EDA<>(2, 1 << 15, //
+          new JSSPUMDAModel(problem.instance, 32L)));
+
+      return list.stream();
+    }
+  },
+
+  /**
+   * the twelfth stage: estimation of distribution algorithm with
+   * local search
+   */
+  STAGE_12 {
+
+    /**
+     * Get a stream of algorithm suppliers for a given problem
+     *
+     * @param problem
+     *          the problem
+     * @return the stream of suppliers
+     */
+    @Override
+    public
+        Stream<Supplier<
+            IMetaheuristic<int[], JSSPCandidateSolution>>>
+        getAlgorithms(//
+            final JSSPMakespanObjectiveFunction problem) {
+
+      final ArrayList<Supplier<
+          IMetaheuristic<int[], JSSPCandidateSolution>>> list =
+              new ArrayList<>();
+
+      list.add(() -> new HybridEDA<>(1, 16, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 64L)));
+      list.add(() -> new HybridEDA<>(2, 16, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 32L)));
+      list.add(() -> new HybridEDA<>(4, 16, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 16L)));
+
+      list.add(() -> new HybridEDA<>(1, 32, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 64L)));
+      list.add(() -> new HybridEDA<>(2, 32, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 32L)));
+      list.add(() -> new HybridEDA<>(4, 32, Integer.MAX_VALUE, //
+          new JSSPUMDAModel(problem.instance, 16L)));
+
       return list.stream();
     }
   };