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
Aracı Değiştirme Oranı	Bireyin Diferansiyel Evrim yöntemini belirlemesi için gerekli olasılığı belirtir.
Assume variables as non negative	Mark to force variables to be positive only.
DE: Çaprazlama Olasılığı	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: Ölçekleme Katsayısı	Çaprazlama sırasında, ölçekleme katsayısı hareketin “hızını” belirler.
Öğrenme Döngüleri	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: Bilişsel Sabit	Sets the importance of the own memory (in particular the best reached point so far).
PS: Daralma Katsayısı	Defines the speed at which the particles/individuals move towards each other.
PS: Mutasyon Olasılığı	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: Sosyal Sabit	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	Eğer devre dışı(varsayılan) ise, BCH Karşılaştırıcısı kullanılacaktır. Bu iki tekili ilk önce bunların kısıt ihlallerine bakmakta ve sadece bunlar eşitse; mevcut çözümlerini ölçmektedir. Etkin ise, ACR Karşılaştırıcısı kullanılır. Mevcut yinelemeye bağlı olan iki bireyi karşılaştırır ve bireylerin uygunluğunu kütüphanelere dair bilinen en kötü çözümler hakkındaki bilgileri ile ölçer.
Rasgele Başlangıç Noktası Kullan	Etkin ise, kitaplık rastgele seçilmiş noktalar ile doldurulur. Etkin değil ise, halihazırda mevcut değerler (kullanıcı tarafından verildiği şekilde) kütüphaneye başvuru noktası olarak eklenir.
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.
Öğrenme Döngüleri	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.
Kitaplık Boyutu	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	Eğer devre dışı(varsayılan) ise, BCH Karşılaştırıcısı kullanılacaktır. Bu iki tekili ilk önce bunların kısıt ihlallerine bakmakta ve sadece bunlar eşitse; mevcut çözümlerini ölçmektedir. Etkin ise, ACR Karşılaştırıcısı kullanılır. Mevcut yinelemeye bağlı olan iki bireyi karşılaştırır ve bireylerin uygunluğunu kütüphanelere dair bilinen en kötü çözümler hakkındaki bilgileri ile ölçer.
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.
Kol ve sınır derinliğini kısıtla	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.

Solver Options

Çözümleyici

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net