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# Solver Algorithms Options

## DEPS Evolutionary Algorithm

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 | Descrición
--- | ---
Taxa de alternancia do axente | Especifica a probabilidade de que un individuo escolla a estratexia de evolución diferencial.
Assume variables as non negative | Mark to force variables to be positive only.
DE: Probabilidade de cruzamento | 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: Factor de escala | Durante o cruzamento, o factor de escala decide sobre a «velocidade» do movemento.
Ciclos de aprendizaxe | 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: Constante cognitiva | Sets the importance of the own memory (in particular the best reached point so far).
PS: Coeficiente de constrición | Defines the speed at which the particles/individuals move towards each other.
PS: Probabilidade de mutación | 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: Constante 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.
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 | Se desactivado (predeterminado), usarase o comparador BCH. O BCH compara dous individuos mirando primeiro as violacións das súsas restricións e se ambos son iguais, mide a súa solución actual. Se activado, usarase o comparador ACR. O ACR compara dous individuos dependendo da iteración actual e da medida sobre a súa bondade respectos do coñecemento das peores solucións coñecidada da biblioteca (verbo da violacións de restricións).
Usar un punto de inicio aleatorio | Se activado, a biblioteca énchese simplemente con puntos escollidos aleatoriamente. Se desactivado, os valores actuais presentes (tal como fosen dados polo usuario) colócanse na biblioteca como punto de referencia.
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 | Descrición
--- | ---
Assume variables as non negative | Mark to force variables to be positive only.
Ciclos de aprendizaxe | 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.
Tamaño da biblioteca | 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 | Se desactivado (predeterminado), usarase o comparador BCH. O BCH compara dous individuos mirando primeiro as violacións das súsas restricións e se ambos son iguais, mide a súa solución actual. Se activado, usarase o comparador ACR. O ACR compara dous individuos dependendo da iteración actual e da medida sobre a súa bondade respectos do coñecemento das peores solucións coñecidada da biblioteca (verbo da violacións de restricións).
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 | Descrición
--- | ---
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.
Limitar a profundidade do algoritmo ramificación e acotación | 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 | Descrición
--- | ---
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.

Temas relacionados

Solver Options

Resolvedor

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net