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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 | Description
--- | ---
Rata de schimbare a agentului | Specifică probabilitatea, ca un individ să aleagă strategia evoluției diferențiale.
Assume variables as non negative | Mark to force variables to be positive only.
DE: Probabilitatea intersectări | 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 scalare | În cazul intersectări, factorul scalar decide despre „viteza” mișcări.
Cicluri de învățare | 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: Constantul cognitiv | Sets the importance of the own memory (in particular the best reached point so far).
PS: Coeficientul de constrângere | Defines the speed at which the particles/individuals move towards each other.
PS: Probabilitatea mutări | 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: Constantul 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 | Dacă este inactiv (implicit), atunci comparatorul BCH este utilizat. It compares two individuals by first looking at their constraint violations and only if those are equal, it measures their current solution. Dacă este activat, comparatorul ACR este utilizat. Acesta compară două indivizi în funcție de iterația actuală, și corectitudinea lor este verificată în funcție de cele mai proaste rezultate din biblioteci (luând în considerare violarea constrângerilor din acestea).
Utilizarea unui punct aleator de pornire | Dacă este activat, biblioteca va fi încărcată cu puncte alese aleator. Dacă este dezactivat, valoarea curentă prezentă (dată de utilizator) va fi inserat în bibliotecă ca punct de referință.
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.
Cicluri de învățare | 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.
Mărimea bibliotecii | 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 | Dacă este inactiv (implicit), atunci comparatorul BCH este utilizat. It compares two individuals by first looking at their constraint violations and only if those are equal, it measures their current solution. Dacă este activat, comparatorul ACR este utilizat. Acesta compară două indivizi în funcție de iterația actuală, și corectitudinea lor este verificată în funcție de cele mai proaste rezultate din biblioteci (luând în considerare violarea constrângerilor din acestea).
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.
Limită de adâncime branch-and-bound | 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.

Related Topics

Solver Options

Solver

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net