Inversed Ru Ascension Save
A metaheuristic optimization framework
Ascension
A metaheuristic optimization framework. See the project homepage for an overview and notable results (please note that many of the described features had to be omitted from the 2.0 release).
How to use the framework
Copy a problem definition module (PDM) from the problems into the project folder, rename it to problem.pas, then compile (I recommend using Lazarus IDE) and run Ascension. Algorithm parameters are specified in the Config.ini file. Several files are created during the optimization process:
| File | Description |
|---|---|
| XX_Status | Status file containing the data about the optimization process (XX is the abbreviated algorithm name) |
| XX_Best | The best solution found during the optimization. |
| Runs | Information about the results of multiple optimization runs |
| Runs_Best | The best solution found during multiple optimization runs |
Included PDMs
| File | Problem |
|---|---|
| Problem_2DHP.pas | Hydrophobic-polar protein folding model |
| Problem_3DNQ.pas | 3D N queens |
| Problem_BinTexGen.pas | Binary texture generation |
| Problem_Chess_Covering.pas | Queen (non)domination, knights covering |
| Problem_NQ.pas | N queens |
| Problem_No_Subsquares.pas | Maximal density subsquare-free arrangements |
| Problem_Peaceable_Queens.pas | Peaceable queens |
| Problem_Still_Life.pas | Maximal density still life |
See the description section inside of each file for more details.
How to create a PDM
You can specify your own problem by creating a problem.pas file that supplies the types and routines required by each metaheuristic (full list can be found in interface.inc). See Problem_NQ.pas from the problems folder for an example of a complete PDM. If you don't need a certain algorithm, the corresponding definitions can be effectively omitted by placing a {$I DummyXX.inc} line at the end of the file.
Config parameters
Global Parameters
| Parameter | Description |
|---|---|
| Algorithm | Optimization algorithm |
| ⇢"SA" | Simulated Annealing |
| ⇢"GA" | Genetic Algorithm |
| ⇢"LS" | Local Search |
| ⇢"TS" | Tabu Search |
| ⇢"CTS" | Cooperative Tabu Search |
| NRuns | Number of independent algorithm runs |
| ScoreToReach | The desired value of the score function, can be used by stopping criteria |
Local Search
| Parameter | Description |
|---|---|
| Mode | Local search mode. |
| ⇢"First" | Pick the first improving move from a move list |
| ⇢"Best" | Pick the best move from a move list |
| ⇢"Chain" | Sort all moves by score, then try to sequentially apply all improving moves in order from best to worst |
| StatusIters | Interval between saving the data about optimization process to a status file, nothing is saved if set to zero |
Tabu Search
| Parameter | Description |
|---|---|
| Iterations | Total number of iterations |
| PopSize | Population size (cooperative variant only) |
| StatusIters | Interval between saving the data about optimization process to a status file, nothing is saved if set to zero |
Simulated Annealing
| Parameter | Description |
|---|---|
| Iterations | Total number of iterations |
| T0 | Initial temperature, used if T0Mode is "Manual" |
| Tfin | Final temperature, used if TfinEBased is No |
| dEmax | Maximal change of energy, used if T0Mode is "EBased" |
| dEmin | Minimal change of energy, used if TfinEBased is Yes |
| T0Mode | Initial temperature selection |
| ⇢"Manual" | Determined by T0 |
| ⇢"EBased" | Calculated based on dEmax |
| ⇢"AutoLow" | Automatic selection, lower temperature mode |
| ⇢"AutoHigh" | Automatic selection, higher temperature mode |
| TfinEBased | Final temperature selection |
| ⇢No | Determined by Tfin |
| ⇢Yes | Calculated based on dEmin |
| Acceptance | Acceptance function |
| ⇢"Exp" | Exp(-xp) |
| ⇢"Power" | 1 / (1 + xp) |
| ⇢"Tsallis" | (1 - (1 - p) x)1 / (1 - p) |
| ⇢"Threshold" | (1 - Sign(x - 1)) / 2 |
| ⇢"Barker" | 1 / (1 + Exp(x)) |
| AcceptanceP | Acceptance function parameter |
| Schedule | Cooling schedule type |
| ⇢"Zero" | T = 0 |
| ⇢"Log" | T ~ 1 / (C + Ln(1 + t / L)) |
| ⇢"Power" | T ~ (1 + t / L)p |
| ⇢"Exp" | T ~ Exp(-(t / L)p) |
| ScheduleP | Cooling schedule parameter |
| NReheat | Number of reheating stages, no reheating if set to 1 |
| FastReheat | Determines the duration of reheating stages |
| ⇢No | Each reheating stage takes the same number of iterations |
| ⇢Yes | Later reheating stages take less iterations |
| Smoothing | The amount of smoothing applied when calculating statistics for the status file |
| StatusIters | Interval between saving the data about optimization process to a status file, nothing is saved if set to zero. |
Genetic Algorithm
See the algorithm page for a detailed description of selection, replacement and acceptance parameters.
| Parameter | Description |
|---|---|
| PopulationSize | Number of individuals in the population |
| Selection | Method of selecting individuals for reproduction |
| SelectionP | Selection parameter |
| Replacement | Method of selecting which individual to replace |
| ReplacementP | Replacement parameter |
| Acceptance | Criterion for determining whether the solution picked by Replacement method actually gets replaced |
| StopCriterion | Criterion for stopping the algorithm |
| ⇢"MaxGens" | Maximal number of generations is reached |
| ⇢"MaxNFE" | Maximal number of function evaluations is reached |
| ⇢"Score" | ScoreToReach is reached |
| StatusGens | Interval in generations between saving the data about optimization process to a status file, nothing is saved if set to zero |
| SaveGens | Interval in generations between saving the population, nothing is saved if set to zero |
Acknowledgements
This project has been supported by the following patrons via Patreon:
- Brian Bucklew
- Anton Shepelev
- Adam Hill
- John Metcalf
- Tomoyuki Naito
Open Source Agenda Badge
