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' Copyright 2020 Daniel Wagner O. de Medeiros
'
' This file is part of DWSIM.
'
' DWSIM is free software: you can redistribute it and/or modify
' it under the terms of the GNU General Public License as published by
' the Free Software Foundation, either version 3 of the License, or
' (at your option) any later version.
'
' DWSIM is distributed in the hope that it will be useful,
' but WITHOUT ANY WARRANTY; without even the implied warranty of
' MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
' GNU General Public License for more details.
'
' You should have received a copy of the GNU General Public License
' along with DWSIM. If not, see <http://www.gnu.org/licenses/>.
Namespace MathEx.OptimizationL
Public Class DE
Public Property Tolerance As Double = 0.0001
Public Property MaxIterations As Integer = 1000
Private _Iterations As Integer = 0
Private fxb As Func(Of Double(), Double)
Private fxg As Func(Of Double(), Double())
Private _error As Double
Private objval, objval0 As Double
Private Solutions As List(Of Double())
Private FunctionValues As List(Of Double)
Public ReadOnly Property Iterations
Get
Return _Iterations
End Get
End Property
Sub New()
End Sub
''' <summary>
''' Minimizes a function value using DE algorithm.
''' </summary>
''' <param name="functionbody">f(x) where x is a vector of doubles, returns the value of the function.</param>
''' <param name="functiongradient">Optional. g(x) where x is a vector of doubles, returns the value of the gradient of the function with respect to each variable.</param>
''' <param name="vars">initial values for x</param>
''' <param name="lbounds">lower bounds for x</param>
''' <param name="ubounds">upper bounds for x</param>
''' <returns>vector of variables corresponding to the function's minimum value.</returns>
Public Function Solve(functionbody As Func(Of Double(), Double), functiongradient As Func(Of Double(), Double()), vars As Double(), Optional lbounds As Double() = Nothing, Optional ubounds As Double() = Nothing) As Double()
Dim obj As Double = 0.0#
Solutions = New List(Of Double())
FunctionValues = New List(Of Double)
fxb = functionbody
fxg = functiongradient
If lbounds Is Nothing Then
lbounds = vars.Clone()
For i As Integer = 0 To lbounds.Length - 1
lbounds(i) = -1.0E+19
Next
End If
If ubounds Is Nothing Then
ubounds = vars.Clone()
For i As Integer = 0 To ubounds.Length - 1
ubounds(i) = 1.0E+19
Next
End If
Dim optimization As New LibOptimization.Optimization.clsOptDE(
New ObjectiveFunction(functionbody, functiongradient, 0.001, vars.Length))
'set initialposition
optimization.InitialPosition = vars
optimization.InitialValueRangeLower = lbounds.Max
optimization.InitialValueRangeUpper = ubounds.Min
'set bpundary
optimization.UpperBounds = ubounds
optimization.LowerBounds = lbounds
'init
optimization.Init()
If optimization.IsRecentError() = True Then
Throw New Exception("Optimization error")
End If
Dim fval As Double
'do optimization
Dim it As Integer = 0
While (optimization.DoIteration(1) = False)
it += 1
If it > MaxIterations Then
Throw New Exception("Optimization error - max iterations reached")
End If
fval = optimization.Result.Eval
If fval < Tolerance Then
Exit While
End If
End While
'get result
Return optimization.Result.ToArray()
End Function
End Class
End Namespace |