Optionen für Solver-Algorithmen

DEPS entwickelnder Algorithmus

DEPS besteht aus zwei unabhängigen Algorithmen: Differentielle Entwicklung und Partikelschwarmoptimierung. Beide eignen sich besonders für numerische Probleme wie die nichtlineare Optimierung und ergänzen sich insofern, als sie ihre anderen Mängel ausgleichen.

Setting

Description

Mittlere Wechselrate

Gibt die Wahrscheinlichkeit an, mit der eine Person die differentielle Entwicklungs-Strategie wählt.

Assume variables as non negative

Mark to force variables to be positive only.

DE: Wechselwahrscheinlichkeit

Legt die Wahrscheinlichkeit fest, dass der einzelne mit dem global besten Punkt kombiniert wird. Wenn kein Wechsel angegeben wird, wird der Punkt aus dem Einzelwert ermittelt.

DE: Skalierungsfaktor

Während des Wechsels entscheidet der Skalierungsfaktor über die „Geschwindigkeit“ der Bewegung.

Lernzyklen

Definiert die Anzahl der Wiederholung, die der Algorithmus ausführen soll. Bei jeder Wiederholung erraten alle Individuen die beste Lösung und teilen ihr Wissen.

PS: Kognitive Konstante

Legt die Wichtigkeit des eigenen Speichers fest (insbesondere den bisher am besten erreichten Punkt).

PS: Verengungskoeffizient

Legt die Geschwindigkeit fest, mit der sich die Partikel/Individuen aufeinander zu bewegen.

PS: Mutationswahrscheinlichkeit

Legt die Wahrscheinlichkeit fest, dass eine Komponente des Partikels nicht zum besten Punkt bewegt wird, sondern zufällig einen neuen Wert aus dem gültigen Bereich für diese Variable auswählt.

PS: Soziale Konstante

Legt die Wichtigkeit des global besten Punktes zwischen allen Partikeln/Individuen fest.

Show Enhanced Solver Status

If enabled, an additional dialog is shown during the solving process which gives information about the current progress, the level of stagnation, the currently best known solution as well as the possibility, to stop or resume the solver.

Size of Swarm

Defines the number of individuals to participate in the learning process. Each individual finds its own solutions and contributes to the overall knowledge.

Stagnation Limit

If this number of individuals found solutions within a close range, the iteration is stopped and the best of these values is chosen as optimal.

Stagnation Tolerance

Defines in what range solutions are considered “similar”.

Use ACR Comparator

If disabled (default), the BCH Comparator is used. It compares two individuals by first looking at their constraint violations and only if those are equal, it measures their current solution.

If enabled, the ACR Comparator is used. It compares two individuals dependent on the current iteration and measures their goodness with knowledge about the libraries worst known solutions (in regard to their constraint violations).

Use Random Starting Point

If enabled, the library is simply filled up with randomly chosen points.

If disabled, the currently present values (as given by the user) are inserted in the library as reference point.

Variable Bounds Guessing

If enabled (default), the algorithm tries to find variable bounds by looking at the starting values.

Variable Bounds Threshold

When guessing variable bounds, this threshold specifies, how the initial values are shifted to build the bounds. For an example how these values are calculated, please refer to the Manual in the Wiki.


SCO Evolutionary Algorithm

Social Cognitive Optimization takes into account the human behavior of learning and sharing information. Each individual has access to a common library with knowledge shared between all individuals.

Setting

Description

Assume variables as non negative

Mark to force variables to be positive only.

Learning Cycles

Defines the number of iterations, the algorithm should take. In each iteration, all individuals make a guess on the best solution and share their knowledge.

Show Enhanced Solver Status

If enabled, an additional dialog is shown during the solving process which gives information about the current progress, the level of stagnation, the currently best known solution as well as the possibility, to stop or resume the solver.

Size of Library

Defines the amount of information to store in the public library. Each individual stores knowledge there and asks for information.

Size of Swarm

Defines the number of individuals to participate in the learning process. Each individual finds its own solutions and contributes to the overall knowledge.

Stagnation Limit

If this number of individuals found solutions within a close range, the iteration is stopped and the best of these values is chosen as optimal.

Stagnation Tolerance

Defines in what range solutions are considered “similar”.

Use ACR Comparator

If disabled (default), the BCH Comparator is used. It compares two individuals by first looking at their constraint violations and only if those are equal, it measures their current solution.

If enabled, the ACR Comparator is used. It compares two individuals dependent on the current iteration and measures their goodness with knowledge about the libraries worst known solutions (in regard to their constraint violations).

Variable Bounds Guessing

If enabled (default), the algorithm tries to find variable bounds by looking at the starting values.

Variable Bounds Threshold

When guessing variable bounds, this threshold specifies, how the initial values are shifted to build the bounds. For an example how these values are calculated, please refer to the Manual in the Wiki.


LibreOffice Linear Solver and CoinMP Linear solver

Setting

Description

Assume variables as integers

Mark to force variables to be integers only.

Assume variables as non negative

Mark to force variables to be positive only.

Epsilon level

Epsilon level. Valid values are in range 0 (very tight) to 3 (very loose). Epsilon is the tolerance for rounding values to zero.

Limit branch-and-bound depth

Specifies the maximum branch-and-bound depth. A positive value means that the depth is absolute. A negative value means a relative branch-and-bound depth limit.

Solver time limit

Sets the maximum time for the algorithm to converge to a solution.


LibreOffice Swarm Non-Linear Solver (Experimental)

Setting

Description

Assume variables as integers

Mark to force variables to be integers only.

Assume variables as non negative

Mark to force variables to be positive only.

Solver time limit

Sets the maximum time for the algorithm to converge to a solution.

Swarm algorithm

Set the swarm algorithm. 0 for differential evolution and 1 for particle swarm optimization. Default is 0.