Contents

# 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
--- | ---
에이전트 스위치 비율 | 개체가 차별적 진화 전략 (Differential Evolution strategy)을 선택할 수 있는 확률을 지정합니다.
Assume variables as non negative | Mark to force variables to be positive only.
DE: 교차 확률 | 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: 배율 | 교차가 일어나고 있을 때 배율은 이동 “속도”를 결정합니다.
교육 주기 | 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: 인지 상수 | Sets the importance of the own memory (in particular the best reached point so far).
PS: 압축 계수 | Defines the speed at which the particles/individuals move towards each other.
PS: 돌연변이 확률 | 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: 사회 상수 | 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 | 비활성화 (디폴트)되어 있으면, BCH 비교기가 사용됩니다. 이는 먼저 2 개의 개체가 제약에서 벗어나는 지를 비교하여 동일한 경우에만 현재 솔루션을 측정합니다. 활성화되어 있는 경우 ACR 비교기가 사용됩니다. 이는 현재 반복되는 검사에 따라 두 개의 개체를 비교하여 최악의 솔루션 (제약에 벗어난 경우와 관련) 라이브러리에 관한 지식을 사용하여 좋은점을 측정합니다.
임의의 시작 지점을 사용 | 활성화되어 있는 경우, 라이브러리는 무작위로 선택한 지점으로 단순하게 채워집니다. 비활성화로 되어 있는 경우, (사용자가 지정한) 현재 표시되어 있는 값이 참조 지점으로 라이브러리에 삽입됩니다.
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.
교육 주기 | 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.
라이브러리 크기 | 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 | 비활성화 (디폴트)되어 있으면, BCH 비교기가 사용됩니다. 이는 먼저 2 개의 개체가 제약에서 벗어나는 지를 비교하여 동일한 경우에만 현재 솔루션을 측정합니다. 활성화되어 있는 경우 ACR 비교기가 사용됩니다. 이는 현재 반복되는 검사에 따라 두 개의 개체를 비교하여 최악의 솔루션 (제약에 벗어난 경우와 관련) 라이브러리에 관한 지식을 사용하여 좋은점을 측정합니다.
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.
분기한정법(branch-and-bound) 깊이 제한 | 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.

관련 주제

Solver Options

해 찾기

Wiki page on non-linear solvers and their algorithms

lp_solve reference guide in sourceforge.net