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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
--- | ---
ۋاكالەتچى ئالماشتۇرۇش نىسبىتى | يەككە گەۋدىنىڭ سان پەرقىنىڭ ئۆزگىرىش ئۇسۇلىنىڭ ئېھتىماللىقىنى بەلگىلەيدۇ.
Assume variables as non negative | Mark to force variables to be positive only.
DE: كىرىشمە ئېھتىماللىقى | 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: نىسبەت كوئېففىنسېنتى | كىرىشتۈرۈش جەريانىدا، يىراق-يېقىنلىق كوئېفىنسىنتى يۆتكىلىش «سۈرئىتى»نى بەلگىلەيدۇ.
ئۆگىنىش دەۋرى | 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: بىلىش تۇراقلىق مىقدارى | Sets the importance of the own memory (in particular the best reached point so far).
PS: پرېسلاش كوئېففىتسېنتى | Defines the speed at which the particles/individuals move towards each other.
PS: توساتتىن ئۆزگىرىش ئېھتىماللىقى | 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: ئىجتىمائىي تۇراقلىق سانى | 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 | ئەگەر چەكلەنگەن (كۆڭۈلدىكى) بولسا BCH سېلىشتۇرغۇچنى ئىشلىتىدۇ. ئۇ ئالدى بىلەن توقۇنۇشنى چەكلەپ يەككە تەننى سېلىشتۇرىدۇ، پەقەت تەڭ بولغاندىلا ئاندىن نۆۋەتتىكى يېشىمىنى تەكشۈرىدۇ. ئەگەر قوزغىتىلغان بولسا ACR سېلىشتۇرغۇچنى ئىشلىتىدۇ. ئۇ نۆۋەتتىكى ئىنتىراتسىيە قېتىم سانى ۋە ئەڭ ناچار يەشمىسىنى تەكشۈرۈپ (ئۇنىڭ توقۇنۇشىنى چەكلەپ) بىلىم ئىگىلەش دەرىجىسىگە ئاساسەن ئىككى يەككە تەننى سېلىشتۇرىدۇ.
تەرتىپسىز باشلىنىش نۇقتىسىنى ئىشلىتىش | ئەگەر قوزغىتىلغان بولسا، تاسادىپىي تاللىغان نۇقتا بويىچە ئامبارنى تولدۇرىدۇ. ئەگەر چەكلەنگەن بولسا، نۆۋەتتە كۆرۈلگەن قىممەت (ئىشلەتكۈچى بەرگەن) پايدىلىنىش نۇقتىسى سۈپىتىدە ئامبارغا قىستۇرۇلىدۇ.
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.
ئۆگىنىش دەۋرى | 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.
ئامبارنىڭ چوڭلۇقى | 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 | ئەگەر چەكلەنگەن (كۆڭۈلدىكى) بولسا BCH سېلىشتۇرغۇچنى ئىشلىتىدۇ. ئۇ ئالدى بىلەن توقۇنۇشنى چەكلەپ يەككە تەننى سېلىشتۇرىدۇ، پەقەت تەڭ بولغاندىلا ئاندىن نۆۋەتتىكى يېشىمىنى تەكشۈرىدۇ. ئەگەر قوزغىتىلغان بولسا ACR سېلىشتۇرغۇچنى ئىشلىتىدۇ. ئۇ نۆۋەتتىكى ئىنتىراتسىيە قېتىم سانى ۋە ئەڭ ناچار يەشمىسىنى تەكشۈرۈپ (ئۇنىڭ توقۇنۇشىنى چەكلەپ) بىلىم ئىگىلەش دەرىجىسىگە ئاساسەن ئىككى يەككە تەننى سېلىشتۇرىدۇ.
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.
تارماق چېگرا چوڭقۇرلۇق چەكلىمىسى | 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