Solver Algorithms Options

Laju Perpindahan Agen

Menentukan probabilitas bagi suatu individu untuk memilih strategi Evolusi Diferensial.

Assume variables as non negative

Mark to force variables to be positive only.

DE: Probabilitas Crossover

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: Faktor Penskalaan

Selama crossover, faktor penskalaan menentukan “kecepatan” pergerakan.

Siklus Belajar

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: Konstanta Kognitif

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

PS: Koefisien Konstriksi

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

PS: Probabilitas Mutasi

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: Konstanta Sosial

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

Bila dinonaktifkan (bawaan), Pembanding BCH dipakai. Ini membandingkan dua individu dengan pertama kali melihat ke pelanggaran pembatasan dan hanya bila mereka sama, itu mengukur solusi mereka saat ini.

Bila difungsikan, Pembanding ACR dipakai. Ini membandingkan dua individu bergantung kepada iterasi saat ini dan mengukur kebaikannya dengan pengetahuan tentang pustaka solusi terburuk yang diketahui (berkaitan dengan pelanggaran pembatasan).

Gunakan Titik Awal Acak

Bila difungsikan, pustaka sekadar dipenuhi dengan titik-titik yang dipilih secara acak.

Bila dinonaktifkan, nilai yang kini ada (yang diberikan oleh pengguna) disisipkan dalam pustaka sebagai titik rujukan.

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.

Siklus Belajar

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.

Ukuran Pustaka

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.

Batas kedalaman 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.