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# Opcions d'algorismes per al solucionador

## Algorisme evolutiu DEPS

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.

Paràmetre | Descripció
--- | ---
Agent Switch Rate | Specifies the probability for an individual to choose the Differential Evolution strategy.
Suposa que les variables no són negatives | Mark to force variables to be positive only.
DE: Crossover Probability | 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.
DE: Scaling Factor | Durant el creuament, el factor d'escalat decideix la «velocitat» del moviment.
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.
PS: constant cognitiva | Sets the importance of the own memory (in particular the best reached point so far).
PS: Constriction Coefficient | Defines the speed at which the particles/individuals move towards each other.
PS: probabilitat de mutació | 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.
PS: constant social | Sets the importance of the global best point between all particles/individuals.
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.
Mida de l'eixam | Defineix el nombre d'individus a participar en el procés d'aprenentatge. Cada individu troba les seves pròpies solucions i contribueix al coneixement general.
Límit d'estancament | 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.
Tolerància d'estancament | Defineix en quin interval les solucions es consideren «similars».
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.
Llindar dels límits de la variable | En deduir els límits de variable, aquest llindar especifica com es canvien els valors inicials per construir els límits. Per un exemple sobre com es calculen aquests valors, consulteu el manual en la 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.

Paràmetre | Descripció
--- | ---
Suposa que les variables no són negatives | 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.
Mida de la biblioteca | Defines the amount of information to store in the public library. Each individual stores knowledge there and asks for information.
Mida de l'eixam | Defineix el nombre d'individus a participar en el procés d'aprenentatge. Cada individu troba les seves pròpies solucions i contribueix al coneixement general.
Límit d'estancament | 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.
Tolerància d'estancament | Defineix en quin interval les solucions es consideren «similars».
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.
Llindar dels límits de la variable | En deduir els límits de variable, aquest llindar especifica com es canvien els valors inicials per construir els límits. Per un exemple sobre com es calculen aquests valors, consulteu el manual en la wiki.

## LibreOffice Linear Solver and CoinMP Linear solver

Paràmetre | Descripció
--- | ---
Suposa que les variables són enters | Mark to force variables to be integers only.
Suposa que les variables no són negatives | Mark to force variables to be positive only.
Genera un informe de sensibilitat | Generates a sensitivity report in a new sheet called "Model_Sensitivity". This option has no effect with the CoinMP Linear solver.
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)

Paràmetre | Descripció
--- | ---
Suposa que les variables són enters | Mark to force variables to be integers only.
Suposa que les variables no són negatives | 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.

Temes relacionats

Opcions del solucionador

Solucionador

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net

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