diff --git a/docs/index.html b/docs/index.html
index ba57888..73dbf40 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -91,17 +91,17 @@ OPL – Optimisation problem library
10 |
2 |
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- |
- |
+ box |
+ >=1 |
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|
no |
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- Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration. |
- |
+ Parameters of the modules of the Automatic Train Operation are optimized; Parameters for the trajectory planning (first module) are maximum velocity (v_max), weighting of objectives (energy, time, comfort) in planning (lambda_e, lambda_s, lambda_t), step width (ds), maximum lateral acceleration (aq_max); as control module a PID controller is implemented, where the ki and kp parameters are optimized. The objectives are minimizing travel time and energy consumption. The evaluation takes several minutes, because the pipeline includes also a simulation. |
|
+ Multi-Objective Pipeline Optimisation and Configuration of Automatic Train Operation Trajectories | Carolin Mensendiek; Henrik Zeipel; Oliver Ludger Preuß; Moritz Vinzent Seiler; Walter Sextro; Heike Trautmann, https://doi.org/10.1145/3795101.3805368 |
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unimodal |
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@@ -117,7 +117,7 @@ OPL – Optimisation problem library
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- |
+ >=1 |
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@@ -214,7 +214,7 @@ OPL – Optimisation problem library
26 |
1 |
noisy |
- box | unknown |
+ unknown | box |
>=14 |
|
noisy |
@@ -292,7 +292,7 @@ OPL – Optimisation problem library
fn_gasoline |
Gasoline direct injection engine design |
Problem |
- integer | continuous |
+ continuous | integer |
14 |
2 |
multi-fidelity |
@@ -1710,11 +1710,11 @@ OPL – Optimisation problem library
|
>=1 |
|
- IOHexperimenter | CEC2013 reference code |
+ CEC2013 reference code | IOHexperimenter |
C++/Python |
|
- https://github.com/IOHprofiler/IOHexperimenter | https://github.com/P-N-Suganthan/CEC2013 |
- IOHprofiler experimenter framework | Suganthan's reference implementation |
+ https://github.com/P-N-Suganthan/CEC2013 | https://github.com/IOHprofiler/IOHexperimenter |
+ Suganthan's reference implementation | IOHprofiler experimenter framework |
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|
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@@ -2178,11 +2178,11 @@ OPL – Optimisation problem library
suite_expobench |
EXPObench |
Suite |
- categorical | integer | continuous |
+ categorical | continuous | integer |
30-405 |
1 |
noisy |
- box | unknown |
+ unknown | box |
>=2 |
|
["observational", "real-life"] |
@@ -2245,7 +2245,7 @@ OPL – Optimisation problem library
|
coco-gbea |
|
- {'34 seconds', '5 seconds'} |
+ {'5 seconds', '34 seconds'} |
https://github.com/ttusar/coco-gbea |
Game-Benchmark for Evolutionary Algorithms (COCO fork) |
|
@@ -2696,7 +2696,7 @@ OPL – Optimisation problem library
|
MECHBench |
Python |
- {'7 minutes', '1 minute'} |
+ {'1 minute', '7 minutes'} |
https://github.com/BayesOptApp/MECHBench |
Structural mechanics design optimization benchmark |
|
@@ -2899,11 +2899,11 @@ OPL – Optimisation problem library
|
20 |
20 |
- pymoo | modact |
+ modact | pymoo |
Python |
{'20ms'} |
- https://github.com/anyoptimization/pymoo | https://github.com/epfl-lamd/modact |
- Multi-objective optimization in Python | EPFL-LAMD modact package |
+ https://github.com/epfl-lamd/modact | https://github.com/anyoptimization/pymoo |
+ EPFL-LAMD modact package | Multi-objective optimization in Python |
|
|
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@@ -3039,7 +3039,7 @@ OPL – Optimisation problem library
suite_re |
RE |
Suite |
- integer | continuous |
+ continuous | integer |
4-14 |
[2, 3, 4, 5, 6, 7, 8, 9] |
|
diff --git a/docs/problems.html b/docs/problems.html
index 04d82b6..abef39c 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -65,17 +65,17 @@
10 |
2 |
|
- |
- |
+ box |
+ >=1 |
|
|
no |
|
|
|
- Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration. |
- |
+ Parameters of the modules of the Automatic Train Operation are optimized; Parameters for the trajectory planning (first module) are maximum velocity (v_max), weighting of objectives (energy, time, comfort) in planning (lambda_e, lambda_s, lambda_t), step width (ds), maximum lateral acceleration (aq_max); as control module a PID controller is implemented, where the ki and kp parameters are optimized. The objectives are minimizing travel time and energy consumption. The evaluation takes several minutes, because the pipeline includes also a simulation. |
|
+ Multi-Objective Pipeline Optimisation and Configuration of Automatic Train Operation Trajectories | Carolin Mensendiek; Henrik Zeipel; Oliver Ludger Preuß; Moritz Vinzent Seiler; Walter Sextro; Heike Trautmann, https://doi.org/10.1145/3795101.3805368 |
|
unimodal |
|
@@ -91,7 +91,7 @@
|
|
|
- |
+ >=1 |
|
|
|
@@ -188,7 +188,7 @@
26 |
1 |
noisy |
- box | unknown |
+ unknown | box |
>=14 |
|
noisy |
@@ -266,7 +266,7 @@
fn_gasoline |
Gasoline direct injection engine design |
Problem |
- integer | continuous |
+ continuous | integer |
14 |
2 |
multi-fidelity |
@@ -1684,11 +1684,11 @@
|
>=1 |
|
- IOHexperimenter | CEC2013 reference code |
+ CEC2013 reference code | IOHexperimenter |
C++/Python |
|
- https://github.com/IOHprofiler/IOHexperimenter | https://github.com/P-N-Suganthan/CEC2013 |
- IOHprofiler experimenter framework | Suganthan's reference implementation |
+ https://github.com/P-N-Suganthan/CEC2013 | https://github.com/IOHprofiler/IOHexperimenter |
+ Suganthan's reference implementation | IOHprofiler experimenter framework |
|
|
|
@@ -2152,11 +2152,11 @@
suite_expobench |
EXPObench |
Suite |
- categorical | integer | continuous |
+ categorical | continuous | integer |
30-405 |
1 |
noisy |
- box | unknown |
+ unknown | box |
>=2 |
|
["observational", "real-life"] |
@@ -2219,7 +2219,7 @@
|
coco-gbea |
|
- {'34 seconds', '5 seconds'} |
+ {'5 seconds', '34 seconds'} |
https://github.com/ttusar/coco-gbea |
Game-Benchmark for Evolutionary Algorithms (COCO fork) |
|
@@ -2670,7 +2670,7 @@
|
MECHBench |
Python |
- {'7 minutes', '1 minute'} |
+ {'1 minute', '7 minutes'} |
https://github.com/BayesOptApp/MECHBench |
Structural mechanics design optimization benchmark |
|
@@ -2873,11 +2873,11 @@
|
20 |
20 |
- pymoo | modact |
+ modact | pymoo |
Python |
{'20ms'} |
- https://github.com/anyoptimization/pymoo | https://github.com/epfl-lamd/modact |
- Multi-objective optimization in Python | EPFL-LAMD modact package |
+ https://github.com/epfl-lamd/modact | https://github.com/anyoptimization/pymoo |
+ EPFL-LAMD modact package | Multi-objective optimization in Python |
|
|
|
@@ -3013,7 +3013,7 @@
suite_re |
RE |
Suite |
- integer | continuous |
+ continuous | integer |
4-14 |
[2, 3, 4, 5, 6, 7, 8, 9] |
|
diff --git a/problems.yaml b/problems.yaml
index b6a2600..fcc539c 100644
--- a/problems.yaml
+++ b/problems.yaml
@@ -2,11 +2,13 @@ fn_ato:
allows_partial_evaluation: no
can_evaluate_objectives_independently: null
code_examples: null
- constraints: null
- description: 'Parameters of the Modules of the Automatic Train Operation are optimized;
- two objectives: minimizing energy consumption and minimizing driving duration.'
+ constraints:
+ - equality: null
+ hard: yes
+ number: null
+ type: box
+ description: 'Parameters of the modules of the Automatic Train Operation are optimized; Parameters for the trajectory planning (first module) are maximum velocity (v_max), weighting of objectives (energy, time, comfort) in planning (lambda_e, lambda_s, lambda_t), step width (ds), maximum lateral acceleration (aq_max); as control module a PID controller is implemented, where the ki and kp parameters are optimized. The objectives are minimizing travel time and energy consumption. The evaluation takes several minutes, because the pipeline includes also a simulation.'
dynamic_type: null
- evaluation_time: null
fidelity_levels: null
implementations: null
instances: null
@@ -17,7 +19,12 @@ fn_ato:
noise_type: null
objectives:
- 2
- references: null
+ references:
+ - authors: ["Carolin Mensendiek", "Henrik Zeipel", "Oliver Ludger Preuß", "Moritz Vinzent Seiler", "Walter Sextro", "Heike Trautmann"]
+ link:
+ type: null
+ url: https://doi.org/10.1145/3795101.3805368
+ - title: "Multi-Objective Pipeline Optimisation and Configuration of Automatic Train Operation Trajectories"
source:
- real-world
tags: null
@@ -1081,6 +1088,13 @@ gen_wmodel:
max: null
min: 1
type: binary
+impl_ato:
+ description: Non-public implementation
+ evaluation_time:
+ - "several minutes"
+ language: Python
+ name: Automatic Train Operation Pipeline
+ type: implementation
impl_beacon:
description: Continuous Bi-objective Benchmark with Explicit Adjustable
COrrelatioN control