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
معدّل تحويل العامل	لتحديد احتمالية اختيار فرد إستراتيجية تطور الفارق.
Assume variables as non negative	Mark to force variables to be positive only.
DE: احتمال العبور	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: عامل التحجيم	خلال التعابر، عامل التحجيم يقرر “سرعة” الحركة.
دورة التعلم	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: ثابت الإدراك	Sets the importance of the own memory (in particular the best reached point so far).
PS: معامل الانقباض	Defines the speed at which the particles/individuals move towards each other.
PS: احتمال الطفرة	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: ثابت اجتماعي	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	إن عُطّل (المبدئيّ)، تستخدم مقارنة BCH. تُقارن بين فردين بالنظر أولا في انتهاكات القيود، و فقط في حال تساوت تقيس حلهم الحالي. إذا تمكين ، يتم استخدام المقارنة ACR. فإنه يقارن بين الأفراد اعتماداً على التكرار الحالي ويقيس الجودة مع المعرفة حول أسوأ حلول المكتبات المعروفة (فيما يتعلق بانتهاكات القيد الخاصة بهم).
استخدام نقطة بداية عشوائياً	إن فُعِّل، المكتبة تملئ ببساطة بنقاط عشوائية مختارة. إن عُطِل، القيم الموجودة حاليًا (كما أعطيت من المستخدم) مُدرجة في المكتبة كنقطة مرجع.
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.
دورة التعلم	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.
حجم المكتبة	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	إن عُطّل (المبدئيّ)، تستخدم مقارنة BCH. تُقارن بين فردين بالنظر أولا في انتهاكات القيود، و فقط في حال تساوت تقيس حلهم الحالي. إذا تمكين ، يتم استخدام المقارنة ACR. فإنه يقارن بين الأفراد اعتماداً على التكرار الحالي ويقيس الجودة مع المعرفة حول أسوأ حلول المكتبات المعروفة (فيما يتعلق بانتهاكات القيد الخاصة بهم).
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.
تحديد عمق الفرع والحد	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

Solver

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net