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/// SwarmOps - Numeric and heuristic optimization for C#
/// Copyright (C) 2003-2011 Magnus Erik Hvass Pedersen.
/// Please see the file license.txt for license details.
/// SwarmOps on the internet: http://www.Hvass-Labs.org/
/// ------------------------------------------------------
namespace SwarmOps.Optimizers
{
/// <summary>
/// Pattern Search (PS), an early variant was originally
/// due to Fermi and Metropolis at the Los Alamos nuclear
/// laboratory, as described by Davidon (1). It is also
/// sometimes called compass search. This is a slightly
/// different variant by Pedersen (2). It works for a
/// wide variety of optimization problems, especially
/// when only few iterations are allowed. It does, however,
/// stagnate rather quickly.
/// </summary>
/// <remarks>
/// References:
/// (1) W.C. Davidon. Variable metric method for minimization.
/// SIAM Journal on Optimization, 1(1):1{17, 1991
/// (2) M.E.H. Pedersen. Tuning & Simplifying Heuristical
/// Optimization. PhD Thesis, University of Southampton,
/// 2010.
/// </remarks>
public class PS : Optimizer
{
#region Constructors.
/// <summary>
/// Construct the object.
/// </summary>
public PS()
: base()
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="problem">Problem to optimize.</param>
public PS(Problem problem)
: base(problem)
{
}
#endregion
#region Base-class overrides, Problem.
/// <summary>
/// Name of the optimizer.
/// </summary>
public override string Name
{
get { return "PS"; }
}
/// <summary>
/// Number of control parameters for optimizer.
/// </summary>
public override int Dimensionality
{
get { return 0; }
}
/// <summary>
/// Control parameter names.
/// </summary>
public override string[] ParameterName
{
get { return null; }
}
/// <summary>
/// Default control parameters.
/// </summary>
public override double[] DefaultParameters
{
get { return null; }
}
/// <summary>
/// Lower search-space boundary for control parameters.
/// </summary>
public override double[] LowerBound
{
get { return null; }
}
/// <summary>
/// Upper search-space boundary for control parameters.
/// </summary>
public override double[] UpperBound
{
get { return null; }
}
#endregion
#region Base-class override, Optimizer.
/// <summary>
/// Perform one optimization run and return the best found solution.
/// </summary>
/// <param name="parameters">Control parameters for the optimizer.</param>
public override Result Optimize(double[] parameters)
{
// Signal beginning of optimization run.
Problem.BeginOptimizationRun();
// Get problem-context.
double[] lowerBound = Problem.LowerBound;
double[] upperBound = Problem.UpperBound;
double[] lowerInit = Problem.LowerInit;
double[] upperInit = Problem.UpperInit;
int n = Problem.Dimensionality;
// Allocate agent position and search-range vectors.
double[] x = new double[n]; // Current position.
double[] y = new double[n]; // Potentially new position.
double[] d = new double[n]; // Search-range.
// Initialize agent-position in search-space.
Tools.InitializeUniform(ref x, lowerInit, upperInit);
// Initialize search-range to full search-space.
Tools.InitializeRange(ref d, lowerBound, upperBound);
// Enforce constraints and evaluate feasibility.
bool feasible = Problem.EnforceConstraints(ref x);
// Compute fitness of initial position.
// This counts as an iteration below.
double fitness = Problem.Fitness(x, feasible);
// Trace fitness of best found solution.
Trace(0, fitness, feasible);
int i;
for (i = 1; Problem.Continue(i, fitness, feasible); i++)
{
// Pick random dimension.
int R = Globals.Random.Index(n);
// Copy current position to new position
x.CopyTo(y, 0);
// Compute new value for randomly chosen dimension.
y[R] += d[R];
// Enforce constraints and evaluate feasibility.
bool newFeasible = Problem.EnforceConstraints(ref y);
// Compute fitness if feasibility (constraint satisfaction) is same or better.
if (Tools.BetterFeasible(feasible, newFeasible))
{
// Compute fitness of new position.
double newFitness = Problem.Fitness(y, fitness, feasible, newFeasible);
// Update best known position, if improvement.
if (Tools.BetterFeasibleFitness(feasible, newFeasible, fitness, newFitness))
{
// Update fitness.
fitness = newFitness;
// Update feasibility.
feasible = newFeasible;
// Update position by swapping array x and y.
// This is necessary because the constraint-handler
// may alter the position y to something different
// from what PS computed it to be. Otherwise we
// could just have updated the R'th dimension of x.
double[] temp = x;
x = y;
y = temp;
}
else // Worse fitness.
{
// Reduce and invert search-range.
d[R] *= -0.5;
}
}
else // Worse feasibility.
{
// Reduce and invert search-range.
d[R] *= -0.5;
}
// Trace fitness of best found solution.
Trace(i, fitness, feasible);
}
// Signal end of optimization run.
Problem.EndOptimizationRun();
// Return best-found solution and fitness.
return new Result(x, fitness, feasible, i);
}
#endregion
}
}
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