fPiKe‖

epbWC‖Solver Algorithms Options

o9GvM‖DEPS Evolutionary Algorithm

DrwBX‖DEPS consists of two independent algorithms: Differential Evolution and Particle Swarm Optimization. Both are especially suited for numerical problems, such as nonlinear optimization, and are complementary to each other in that they even out each other’s shortcomings.

Setting

Description

EFbzc‖Agent Switch Rate

E3ZoK‖Specifies the probability for an individual to choose the Differential Evolution strategy.

Assume variables as non negative

Mark to force variables to be positive only.

DbnUB‖DE: Crossover Probability

phaC2‖Defines the probability of the individual being combined with the globally best point. If crossover is not used, the point is assembled from the own memory of the individual.

G4GC9‖DE: Scaling Factor

qKt78‖During crossover, the scaling factor decides about the “speed” of movement.

HPPHg‖Learning Cycles

ED86j‖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.

M5Ka8‖PS: Cognitive Constant

bVENc‖Sets the importance of the own memory (in particular the best reached point so far).

ujBHP‖PS: Constriction Coefficient

Z86BZ‖Defines the speed at which the particles/individuals move towards each other.

nFnAu‖PS: Mutation Probability

fGiKi‖Defines the probability, that instead of moving a component of the particle towards the best point, it randomly chooses a new value from the valid range for that variable.

nn4Ms‖PS: Social Constant

nAD2Z‖Sets the importance of the global best point between all particles/individuals.

GAGDE‖Show Enhanced Solver Status

na8Ee‖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.

3LaZ7‖Size of Swarm

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

r62GH‖Stagnation Limit

LDNEL‖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.

vGYwe‖Stagnation Tolerance

wqeXY‖Defines in what range solutions are considered “similar”.

jKuiG‖Use ACR Comparator

D8e8D‖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.

7LWoa‖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).

GZ7C2‖Use Random Starting Point

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

oCPc4‖If disabled, the currently present values (as given by the user) are inserted in the library as reference point.

ctLqK‖Variable Bounds Guessing

rc95a‖If enabled (default), the algorithm tries to find variable bounds by looking at the starting values.

JctSA‖Variable Bounds Threshold

NNyfL‖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.


g7v8S‖SCO Evolutionary Algorithm

LaQds‖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.

wovoy‖Learning Cycles

wgKE5‖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.

wrasx‖Size of Library

wyuAJ‖ 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

D8e8D‖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.

7LWoa‖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.


FAW7L‖LibreOfficeDev Linear Solver and CoinMP Linear solver

PNEaC‖Setting

DhVRA‖Description

MqHfE‖Assume variables as integers

Javmc‖Mark to force variables to be integers only.

yie3u‖Assume variables as non negative

ij2he‖Mark to force variables to be positive only.

uEDEh‖Epsilon level

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

rtCLo‖Limit branch-and-bound depth

QnDUS‖ 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.

pnUYs‖Solver time limit

PPtR8‖Sets the maximum time for the algorithm to converge to a solution.


dBQSw‖LibreOfficeDev 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.

TFadK‖Swarm algorithm

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