Contents

# 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
--- | ---
Aģenta pārslēgšanas biežums | Norāda varbūtību indivīdam izvēlēties diferenciālās evolūcijas jeb DE stratēģiju.
Assume variables as non negative | Mark to force variables to be positive only.
DE: pārejas varbūtība | 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: mērogošanas koeficients | Pārejas laikā mērogošanas koeficients nosaka kustības "ātrumu".
Mācīšanās cikli | 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: kognitīvā konstante | Sets the importance of the own memory (in particular the best reached point so far).
PS: sašaurināšanas koeficients | Defines the speed at which the particles/individuals move towards each other.
PS: mutācijas varbūtība | 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: sociālā konstante | 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 | Ja izslēgts (noklusējums), tiek lietots BCH komparators. Tas salīdzina divus indivīdus, vispirms apskatot to ierobežojumu pārkāpumus, un tikai tad, ja tie ir vienādi, izmēra to pašreizējos risinājumus. Ja ieslēgts, tiks lietots ACR komparators. Tas salīdzina divus indivīdus atkarībā no pašreizējās iterācijas un mēra to labumu, ar zināšanām par bibliotēkai sliktāko zināmo risinājumu (attiecībā pret to ierobežojumu pārkāpumiem).
Lietot nejaušu sākuma punktu | Ja ieslēgts, bibliotēka tiek vienkārši aizpildīta ar nejauši izvēlētiem punktiem. Ja izslēgts, pašlaik esošās vērtības (kādas ir norādījis lietotājs) tiek ievietotas bibliotēkā kā atskaites punkti.
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.
Mācīšanās cikli | 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.
Bibliotēkas izmērs | 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 | Ja izslēgts (noklusējums), tiek lietots BCH komparators. Tas salīdzina divus indivīdus, vispirms apskatot to ierobežojumu pārkāpumus, un tikai tad, ja tie ir vienādi, izmēra to pašreizējos risinājumus. Ja ieslēgts, tiks lietots ACR komparators. Tas salīdzina divus indivīdus atkarībā no pašreizējās iterācijas un mēra to labumu, ar zināšanām par bibliotēkai sliktāko zināmo risinājumu (attiecībā pret to ierobežojumu pārkāpumiem).
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.
Ierobežot 'zaru un robežu' dziļumu | 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.

Saistītie temati

Solver Options

Risinātājs

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net