Module org.cicirello.chips_n_salsa
Package org.cicirello.search.hc

Class SteepestDescentHillClimber<T extends Copyable<T>>

java.lang.Object
org.cicirello.search.hc.SteepestDescentHillClimber<T>
Type Parameters:
T - The type of object under optimization.
All Implemented Interfaces:
Splittable<TrackableSearch<T>>, Metaheuristic<T>, SimpleLocalMetaheuristic<T>, SimpleMetaheuristic<T>, TrackableSearch<T>
public final class SteepestDescentHillClimber<T extends Copyable<T>> extends Object
This class implements steepest descent hill climbing. In hill climbing, the search typically begins at a randomly generated candidate solution. It then iterates over the so called "neighbors" of the current candidate solution, choosing to move to a neighbor that locally appears better than the current candidate (i.e., has a lower cost value). This is then repeated until the search terminates when all neighbors of the current candidate solution are worse than the current candidate solution.

In steepest descent hill climbing, the search always iterates over all of the neighbors of the current candidate before deciding which to move to. It then picks the neighbor with lowest cost value from among all those neighbors whose cost is lower than the current cost. If no such neighbor exists, the search terminates with the current solution.

  • Constructor Details

    • SteepestDescentHillClimber

      public SteepestDescentHillClimber(OptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)
      Constructs a steepest descent hill climber object for real-valued optimization problem.
      Parameters:
      problem - An instance of an optimization problem to solve.
      mutation - A mutation operator.
      initializer - The source of random initial states for each hill climb.
      tracker - A ProgressTracker object, which is used to keep track of the best solution found during the run, the time when it was found, and other related data.
      Throws:
      NullPointerException - if any of the parameters are null.

    • SteepestDescentHillClimber

      public SteepestDescentHillClimber(IntegerCostOptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)
      Constructs a steepest descent hill climber object for integer-valued optimization problem.
      Parameters:
      problem - An instance of an optimization problem to solve.
      mutation - A mutation operator.
      initializer - The source of random initial states for each hill climb.
      tracker - A ProgressTracker object, which is used to keep track of the best solution found during the run, the time when it was found, and other related data.
      Throws:
      NullPointerException - if any of the parameters are null.

    • SteepestDescentHillClimber

      public SteepestDescentHillClimber(OptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer)
      Constructs a steepest descent hill climber object for real-valued optimization problem. A ProgressTracker is created for you.
      Parameters:
      problem - An instance of an optimization problem to solve.
      mutation - A mutation operator.
      initializer - The source of random initial states for each hill climb.
      Throws:
      NullPointerException - if any of the parameters are null.

    • SteepestDescentHillClimber

      public SteepestDescentHillClimber(IntegerCostOptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer)
      Constructs a steepest descent hill climber object for integer-valued optimization problem. A ProgressTracker is created for you.
      Parameters:
      problem - An instance of an optimization problem to solve.
      mutation - A mutation operator.
      initializer - The source of random initial states for each hill climb.
      Throws:
      NullPointerException - if any of the parameters are null.

  • Method Details

    • split

      public SteepestDescentHillClimber<T> split()
      Description copied from interface: Splittable
      Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms. The state of the object that is returned may or may not be identical to that of the original. Thus, this is a distinct concept from the functionality of the Copyable interface. Classes that implement this interface must ensure that the object returned performs the same functionality, and that it does not share any state data that would be either unsafe or inefficient for concurrent access by multiple threads. The split method is allowed to simply return the this reference, provided that it is both safe and efficient for multiple threads to share a single copy of the Splittable object. The intention is to provide a multithreaded search with the capability to provide spawned threads with their own distinct search operators. Such multithreaded algorithms can call the split method for each thread it spawns to generate a functionally identical copy of the operator, but with independent state.
      Specified by:
      split in interface Metaheuristic<T extends Copyable<T>>
      Specified by:
      split in interface SimpleLocalMetaheuristic<T extends Copyable<T>>
      Specified by:
      split in interface SimpleMetaheuristic<T extends Copyable<T>>
      Specified by:
      split in interface Splittable<T extends Copyable<T>>
      Returns:
      A functionally identical copy of the object, or a reference to this if it is both safe and efficient for multiple threads to share a single instance of this Splittable object.

    • getProblem

      public final Problem<T> getProblem()
      Description copied from interface: TrackableSearch
      Gets a reference to the problem that this search is solving.
      Returns:
      a reference to the problem.

    • optimize

      public final SolutionCostPair<T> optimize()
      Description copied from interface: SimpleMetaheuristic
      Executes a single run of a metaheuristic whose run length cannot be specified (e.g., a hill climber that terminates when it reaches a local optima, or a stochastic sampler that terminates when it constructs one solution, etc). If this method is called multiple times, each call is randomized in some algorithm dependent way (e.g., a hill climber begins at a new randomly generated starting solution), and reinitializes any control parameters that may have changed during the previous call to optimize to the start of run state.
      Specified by:
      optimize in interface SimpleMetaheuristic<T extends Copyable<T>>
      Returns:
      The current solution at the end of this run and its cost, which may or may not be the same as the solution contained in this metaheuristic's ProgressTracker, which contains the best of all runs. Returns null if the run did not execute, such as if the ProgressTracker already contains the theoretical best solution.

    • optimize

      public final SolutionCostPair<T> optimize(T start)
      Description copied from interface: SimpleLocalMetaheuristic
      Executes a single run of a metaheuristic, whose run length cannot be specified (e.g., a hill climber that terminates when it reaches a local optima), beginning at a specified solution. If this method is called multiple times, each call reinitializes any control parameters that may have changed during the previous call to optimize to the start of run state.
      Specified by:
      optimize in interface SimpleLocalMetaheuristic<T extends Copyable<T>>
      Parameters:
      start - The desired starting solution.
      Returns:
      The current solution at the end of this run and its cost, which may or may not be the same as the solution contained in this metaheuristic's ProgressTracker, which contains the best of all runs. Returns null if the run did not execute, such as if the ProgressTracker already contains the theoretical best solution.

    • optimize

      public final SolutionCostPair<T> optimize(int numRestarts)
      Executes multiple restarts of the hill climber. Each restart begins from a new random starting solution. Returns the best solution across the restarts.
      Specified by:
      optimize in interface Metaheuristic<T extends Copyable<T>>
      Parameters:
      numRestarts - The number of restarts of the hill climber.
      Returns:
      The best solution of this set of restarts, which may or may not be the same as the solution contained in this hill climber's ProgressTracker, which contains the best of all runs across all calls to the various optimize methods. Returns null if no runs executed, such as if the ProgressTracker already contains the theoretical best solution.

    • getProgressTracker

      public final ProgressTracker<T> getProgressTracker()
      Description copied from interface: TrackableSearch
      Gets the ProgressTracker object that is in use for tracking search progress. The object returned by this method contains the best solution found during the search (including across multiple concurrent runs if the search is multithreaded, or across multiple restarts if the run methods were called multiple times), as well as cost of that solution, among other information. See the ProgressTracker documentation for more information about the search data tracked by this object.
      Specified by:
      getProgressTracker in interface TrackableSearch<T extends Copyable<T>>
      Returns:
      the ProgressTracker in use by this metaheuristic.

    • setProgressTracker

      public final void setProgressTracker(ProgressTracker<T> tracker)
      Description copied from interface: TrackableSearch
      Sets the ProgressTracker object that is in use for tracking search progress. Any previously set ProgressTracker is replaced by this one.
      Specified by:
      setProgressTracker in interface TrackableSearch<T extends Copyable<T>>
      Parameters:
      tracker - The new ProgressTracker to set. The tracker must not be null. This method does nothing if tracker is null.

    • getTotalRunLength

      public final long getTotalRunLength()
      Gets the total run length, where run length is number of candidate solutions generated by the hill climber. This is the total run length across all calls to the search.
      Specified by:
      getTotalRunLength in interface TrackableSearch<T extends Copyable<T>>
      Returns:
      the total number of candidate solutions generated by the search, across all calls to the various optimize methods.