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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/
/// ------------------------------------------------------
using System.Diagnostics;
namespace SwarmOps.Optimizers.Parallel
{
/// <summary>
/// Parallel version of MOL which computes the fitness of its agents
/// in parallel. Assumes the fitness function is thread-safe. Should
/// only be used with very time-consuming optimization problems otherwise
/// basic MOL will execute faster because of less overhead.
/// </summary>
public class MOL : Optimizer
{
#region Constructors.
/// <summary>
/// Construct the object.
/// </summary>
public MOL()
: this(1)
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="problem">Problem to optimize.</param>
public MOL(Problem problem)
: this(1, problem)
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="numAgentsMultiple">Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, ...</param>
public MOL(int numAgentsMultiple)
: base()
{
NumAgentsMultiple = numAgentsMultiple;
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="numAgentsMultiple">Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, etc.</param>
/// <param name="problem">Problem to optimize.</param>
public MOL(int numAgentsMultiple, Problem problem)
: base(problem)
{
NumAgentsMultiple = numAgentsMultiple;
}
#endregion
#region Sets of control parameters.
/// <summary>
/// Control parameters.
/// </summary>
public struct Parameters
{
/// <summary>
/// Control parameters tuned for all benchmark problems in
/// 5 dimensions and 10000 fitness evaluations in one optimization run.
/// </summary>
public static readonly double[] AllBenchmarks5Dim10000Iter = { 32.0, -0.3319, 5.91 };
/// <summary>
/// Control parameters tuned for all benchmark problems in
/// 30 dimensions and 60000 fitness evaluations in one optimization run.
/// </summary>
public static readonly double[] AllBenchmarks30Dim60000Iter = { 164.0, -0.4241, 2.7729 };
}
#endregion
#region Get control parameters.
/// <summary>
/// Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, etc.
/// </summary>
public int NumAgentsMultiple
{
get;
protected set;
}
/// <summary>
/// Get parameter, Number of agents, aka. swarm-size.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public int GetNumAgents(double[] parameters)
{
int numAgents = (int)System.Math.Round(parameters[0], System.MidpointRounding.AwayFromZero);
// Ensure numAgents falls on desired multiple.
numAgents--;
int mod = numAgents % NumAgentsMultiple;
numAgents += NumAgentsMultiple - mod;
return numAgents;
}
/// <summary>
/// Get parameter, Omega.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public double GetOmega(double[] parameters)
{
return parameters[1];
}
/// <summary>
/// Get parameter, Phi.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public double GetPhi(double[] parameters)
{
return parameters[2];
}
#endregion
#region Base-class overrides, Problem.
/// <summary>
/// Name of the optimizer.
/// </summary>
public override string Name
{
get { return "MOL-Par" + NumAgentsMultiple; }
}
/// <summary>
/// Number of control parameters for optimizer.
/// </summary>
public override int Dimensionality
{
get { return 3; }
}
string[] _parameterName = { "S", "omega", "phi" };
/// <summary>
/// Control parameter names.
/// </summary>
public override string[] ParameterName
{
get { return _parameterName; }
}
/// <summary>
/// Default control parameters.
/// </summary>
public override double[] DefaultParameters
{
get { return Parameters.AllBenchmarks30Dim60000Iter; }
}
static readonly double[] _lowerBound = { 1.0, -2.0, -4.0 };
/// <summary>
/// Lower search-space boundary for control parameters.
/// </summary>
public override double[] LowerBound
{
get { return _lowerBound; }
}
static readonly double[] _upperBound = { 300.0, 2.0, 6.0 };
/// <summary>
/// Upper search-space boundary for control parameters.
/// </summary>
public override double[] UpperBound
{
get { return _upperBound; }
}
#endregion
#region Base-class overrides, 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)
{
Debug.Assert(parameters != null && parameters.Length == Dimensionality);
// Signal beginning of optimization run.
Problem.BeginOptimizationRun();
// Retrieve parameter specific to this optimizer.
int numAgents = GetNumAgents(parameters);
double omega = GetOmega(parameters);
double phi = GetPhi(parameters);
Debug.Assert(numAgents > 0);
// 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 positions, velocities, etc.
double[][] agents = Tools.NewMatrix(numAgents, n);
double[][] velocities = Tools.NewMatrix(numAgents, n);
double[] fitness = new double[numAgents];
bool[] feasible = new bool[numAgents];
// Allocate velocity boundaries.
double[] velocityLowerBound = new double[n];
double[] velocityUpperBound = new double[n];
// Best-found position and fitness.
double[] g = new double[n];
double gFitness = Problem.MaxFitness;
bool gFeasible = false;
// Iteration variables.
int i, j, k;
// Initialize velocity boundaries.
for (k = 0; k < n; k++)
{
double range = System.Math.Abs(upperBound[k] - lowerBound[k]);
velocityLowerBound[k] = -range;
velocityUpperBound[k] = range;
}
// Initialize all agents. (Non-parallel)
for (j = 0; j < numAgents; j++)
{
// Refer to the j'th agent as x and v.
double[] x = agents[j];
double[] v = velocities[j];
// Initialize velocity.
Tools.InitializeUniform(ref v, velocityLowerBound, velocityUpperBound);
// Initialize agent-position in search-space.
Tools.InitializeUniform(ref x, lowerInit, upperInit);
// Enforce constraints and evaluate feasibility.
feasible[j] = Problem.EnforceConstraints(ref x);
}
// Compute fitness of initial position. (Parallel)
// This counts as iterations below.
System.Threading.Tasks.Parallel.For(0, numAgents, Globals.ParallelOptions, (jPar) =>
{
fitness[jPar] = Problem.Fitness(agents[jPar], feasible[jPar]);
});
// Update swarm's best known position. (Non-parallel)
for (j = 0; j < numAgents; j++)
{
if (Tools.BetterFeasibleFitness(gFeasible, feasible[j], gFitness, fitness[j]))
{
agents[j].CopyTo(g, 0);
gFitness = fitness[j];
gFeasible = feasible[j];
}
// Trace fitness of best found solution.
Trace(j, gFitness, gFeasible);
}
// Perform actual optimization iterations.
for (i = numAgents; Problem.Continue(i, gFitness, gFeasible); )
{
// Update agent positions. (Non-parallel)
for (j = 0; j < numAgents; j++)
{
// Refer to the j'th agent as x and v.
double[] x = agents[j];
double[] v = velocities[j];
// Pick random weight.
double r = Globals.Random.Uniform();
// Update velocity.
for (k = 0; k < n; k++)
{
v[k] = omega * v[k] + phi * r * (g[k] - x[k]);
}
// Fix denormalized floating-point values in velocity.
Tools.Denormalize(ref v);
// Enforce velocity bounds before updating position.
Tools.Bound(ref v, velocityLowerBound, velocityUpperBound);
// Update position.
for (k = 0; k < n; k++)
{
x[k] = x[k] + v[k];
}
}
// Compute new fitness. (Parallel)
System.Threading.Tasks.Parallel.For(0, numAgents, Globals.ParallelOptions, (jPar) =>
{
// Enforce constraints and evaluate feasibility.
feasible[jPar] = Problem.EnforceConstraints(ref agents[jPar]);
// Compute fitness if feasibility is same or better.
if (Tools.BetterFeasible(gFeasible, feasible[jPar]))
{
fitness[jPar] = Problem.Fitness(agents[jPar], gFitness, gFeasible, feasible[jPar]);
}
});
// Update swarm's best known position. (Non-parallel)
for (j = 0; j < numAgents; j++, i++)
{
if (Tools.BetterFeasibleFitness(gFeasible, feasible[j], gFitness, fitness[j]))
{
agents[j].CopyTo(g, 0);
gFitness = fitness[j];
gFeasible = feasible[j];
}
// Trace fitness of best found solution.
Trace(i, gFitness, gFeasible);
}
}
// Signal end of optimization run.
Problem.EndOptimizationRun();
// Return best-found solution and fitness.
return new Result(g, gFitness, gFeasible, i);
}
#endregion
}
} |