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
エージェントスイッチレート	個体が Differential Evolution strategy を選択する確率を設定します。
Assume variables as non negative	Mark to force variables to be positive only.
DE: 交差確率	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: スケール係数	交差が起こっている際に、スケール係数は動きの「速さ」を決定します。
学習周期	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: 認識定数	Sets the importance of the own memory (in particular the best reached point so far).
PS: 圧縮係数	Defines the speed at which the particles/individuals move towards each other.
PS: 突然変異確率	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: 社会定数	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	無効になっているときは（デフォルトでは無効です）、 BCH 比較器が使用されます。この比較器は2つの個体をまずその制約からの逸脱を見て比較し、それらが等しいときだけ、現在の解を測ります。有効になっているときは、 ACR 比較器が使用されます。
ランダムな開始点を使用する	有効になっているときは、ライブラリはランダムに選択されたデータで単純に満たされます。無効になっているときは、（ユーザーによって与えられる）現在選択されている値が参照点としてライブラリに挿入されます。
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.
学習周期	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.
ライブラリのサイズ	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	無効になっているときは（デフォルトでは無効です）、 BCH 比較器が使用されます。この比較器は2つの個体をまずその制約からの逸脱を見て比較し、それらが等しいときだけ、現在の解を測ります。有効になっているときは、 ACR 比較器が使用されます。
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.
分岐と境界の深さを制限	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

ソルバー

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net