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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
--- | ---
Agentin vaihtoaste | Määrää todennäköisyyden, jolla yksilö valitsee differentiaalievoluutiostrategian.
Assume variables as non negative | Mark to force variables to be positive only.
DE: Sekoitustodennäköisyys | 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: Skaalaustekijä | Tämä skaalaustekijä määrää sekoituksen siirtymänopeuden.
Oppimissyklit | 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: Kognitiivinen vakio | Sets the importance of the own memory (in particular the best reached point so far).
PS: Supistumiskerroin | Defines the speed at which the particles/individuals move towards each other.
PS: Mutaatiotodennäköisyys | 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: Sosiaalinen vakio | 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 | Jos ei valittu (oletus), käytetään BCH-vertailijaa. Se vertailee yksilöitä ensisijaisesti sen mukaan, kuinka pahasti ne rikkovat rajoitteita. Vain jos rajoitteiden rikkomisessa ei ole eroa, vertaillaan ratkaisun hyvyyttä. Jos valittu, käytetään ACR-vertailijaa. Se vertailee yksilöitä nykyisen iteraation pohjalta ja arvioi ratkaisujen hyvyyttä vertaamalla niitä kirjaston huonoimpiin tunnettuihin ratkaisuihin (rajoitteiden rikkoutumisen mukaan).
Käytä satunnaista aloituspistettä | Jos valittu, kirjasto täytetään satunnaisilla aloituspisteillä. Jos ei valittu, nykyisiä (käyttäjän antamia) arvoja käytetään kirjaston aloituspisteinä.
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.
Oppimissyklit | 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.
Kirjaston koko | 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 | Jos ei valittu (oletus), käytetään BCH-vertailijaa. Se vertailee yksilöitä ensisijaisesti sen mukaan, kuinka pahasti ne rikkovat rajoitteita. Vain jos rajoitteiden rikkomisessa ei ole eroa, vertaillaan ratkaisun hyvyyttä. Jos valittu, käytetään ACR-vertailijaa. Se vertailee yksilöitä nykyisen iteraation pohjalta ja arvioi ratkaisujen hyvyyttä vertaamalla niitä kirjaston huonoimpiin tunnettuihin ratkaisuihin (rajoitteiden rikkoutumisen mukaan).
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.
Rajoita hakupuumenetelmän syvyyttä | 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.

Aiheeseen liittyvää

Solver Options

Ratkaisin

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net