Solver Algorithms Options

Taxa de alternância do agente

Especifica a probabilidade de um individuo escolher a estratégia de Evolução diferencial.

Assume variables as non negative

Mark to force variables to be positive only.

DE: Probabilidade de cruzamento

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: Fator de escala

Durante o cruzamento, o fator de escala determina a velocidade do movimento.

Ciclos de aprendizagem

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: Constante cognitiva

Sets the importance of the own memory (in particular the best reached point so far).

PS: Coeficiente de restrição

Defines the speed at which the particles/individuals move towards each other.

PS: Probabilidade de mutação

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: Constante social

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

Se inativa (padrão), é utilizado o comparador BCH. Compara dois indivíduos, verificando as violações de restrição e se ambos os valores forem iguais, é que calcula a solução atual.

Se ativo, será utilizado o comparador ACR. O ACR compara dois indivíduos dependendo da iteração atual e de acordo com o seu grau de conhecimento das piores soluções conhecidas da biblioteca (em relação às suas violações de restrições).

Utilizar ponto inicial aleatório

Se ativa, a biblioteca é preenchida com pontos aleatórios.

Se desativado, os valores atuais (indicados pelo utilizador) serão inseridos na biblioteca como pontos de referência.

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.

Assume variables as non negative

Mark to force variables to be positive only.

Ciclos de aprendizagem

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.

Tamanho da biblioteca

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

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.

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.

Limitar profundidade branch-and-bound

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.

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.