Examples of aima.core.search.local.SimulatedAnnealingSearch

aima.core.search.local.SimulatedAnnealingSearch
Artificial Intelligence A Modern Approach (3rd Edition): Figure 4.5, page 126.
```
 function SIMULATED-ANNEALING(problem, schedule) returns a solution state current <- MAKE-NODE(problem.INITIAL-STATE) for t = 1 to INFINITY do T <- schedule(t) if T = 0 then return current next <- a randomly selected successor of current /\E <- next.VALUE - current.value if /\E > 0 then current <- next else current <- next only with probability eˆ(/\E/T) 
```
Figure 4.5 The simulated annealing search algorithm, a version of stochastic hill climbing where some downhill moves are allowed. Downhill moves are accepted readily early in the annealing schedule and then less often as time goes on. The schedule input determines the value of the temperature T as a function of time. @author Ravi Mohan @author Mike Stampone

    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(
          new AttackingPairsHeuristic());
      SearchAgent agent = new SearchAgent(problem, search);


      System.out.println();
      printActions(agent.getActions());
      System.out.println("Search Outcome=" + search.getOutcome());
      System.out.println("Final State=\n" + search.getLastSearchState());
      printInstrumentation(agent.getInstrumentation());
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

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    System.out.println("\nEightPuzzleDemo Simulated Annealing  Search -->");
    try {
      Problem problem = new Problem(random1, EightPuzzleFunctionFactory
          .getActionsFunction(), EightPuzzleFunctionFactory
          .getResultFunction(), new EightPuzzleGoalTest());
      SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(
          new ManhattanHeuristicFunction());
      SearchAgent agent = new SearchAgent(problem, search);
      printActions(agent.getActions());
      System.out.println("Search Outcome=" + search.getOutcome());
      System.out.println("Final State=\n" + search.getLastSearchState());
      printInstrumentation(agent.getInstrumentation());
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

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  @Test
  public void testForGivenNegativeDeltaEProbabilityOfAcceptanceDecreasesWithDecreasingTemperature() {
    // this isn't very nice. the object's state is uninitialized but is ok
    // for this test.
    SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(null);
    int deltaE = -1;
    double higherTemperature = 30.0;
    double lowerTemperature = 29.5;


    Assert.assertTrue(search.probabilityOfAcceptance(lowerTemperature,
        deltaE) < search.probabilityOfAcceptance(higherTemperature,
        deltaE));
  }

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Related Classes of aima.core.search.local.SimulatedAnnealingSearch

aima.core.search.framework.Node

aima.gui.demo.search.EightPuzzleDemo

aima.gui.demo.search.NQueensDemo

aima.test.core.unit.search.local.SimulatedAnnealingSearchTest

java.util.Random

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