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Update ATO information #209

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e57fd1c
Update ATO information
kvdblom Aug 30, 2026
a043bf3
chore: Auto-update HTML files
kvdblom Aug 30, 2026
78bf985
Merge branch 'main' into 208-update-ato-entry
kvdblom Sep 16, 2026
fb5b34f
chore: Auto-update HTML files
kvdblom Sep 16, 2026
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36 changes: 18 additions & 18 deletions docs/index.html
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Expand Up @@ -91,17 +91,17 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td>10</td>
<td>2</td>
<td></td>
<td></td>
<td></td>
<td>box</td>
<td>>=1</td>
<td></td>
<td></td>
<td>no</td>
<td></td>
<td></td>
<td></td>
<td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
<td></td>
<td>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.</td>
<td></td>
<td>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, <a href="https://doi.org/10.1145/3795101.3805368">https://doi.org/10.1145/3795101.3805368</a></td>
<td></td>
<td>unimodal</td>
<td></td>
Expand All @@ -117,7 +117,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td></td>
<td></td>
<td></td>
<td></td>
<td>>=1</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -214,7 +214,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td>26</td>
<td>1</td>
<td>noisy</td>
<td>box | unknown</td>
<td>unknown | box</td>
<td>>=14</td>
<td></td>
<td>noisy</td>
Expand Down Expand Up @@ -292,7 +292,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td>fn_gasoline</td>
<td>Gasoline direct injection engine design</td>
<td>Problem</td>
<td>integer | continuous</td>
<td>continuous | integer</td>
<td>14</td>
<td>2</td>
<td>multi-fidelity</td>
Expand Down Expand Up @@ -1710,11 +1710,11 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td></td>
<td>>=1</td>
<td></td>
<td>IOHexperimenter | CEC2013 reference code</td>
<td>CEC2013 reference code | IOHexperimenter</td>
<td>C++/Python</td>
<td></td>
<td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
<td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
<td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
<td>Suganthan's reference implementation | IOHprofiler experimenter framework</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -2178,11 +2178,11 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td>suite_expobench</td>
<td>EXPObench</td>
<td>Suite</td>
<td>categorical | integer | continuous</td>
<td>categorical | continuous | integer</td>
<td>30-405</td>
<td>1</td>
<td>noisy</td>
<td>box | unknown</td>
<td>unknown | box</td>
<td>>=2</td>
<td></td>
<td>["observational", "real-life"]</td>
Expand Down Expand Up @@ -2245,7 +2245,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td></td>
<td>coco-gbea</td>
<td></td>
<td>{'34 seconds', '5 seconds'}</td>
<td>{'5 seconds', '34 seconds'}</td>
<td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
<td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
<td></td>
Expand Down Expand Up @@ -2696,7 +2696,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td></td>
<td>MECHBench</td>
<td>Python</td>
<td>{'7 minutes', '1 minute'}</td>
<td>{'1 minute', '7 minutes'}</td>
<td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
<td>Structural mechanics design optimization benchmark</td>
<td></td>
Expand Down Expand Up @@ -2899,11 +2899,11 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td></td>
<td>20</td>
<td>20</td>
<td>pymoo | modact</td>
<td>modact | pymoo</td>
<td>Python</td>
<td>{'20ms'}</td>
<td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a> | <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a></td>
<td>Multi-objective optimization in Python | EPFL-LAMD modact package</td>
<td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
<td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -3039,7 +3039,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
<td>suite_re</td>
<td>RE</td>
<td>Suite</td>
<td>integer | continuous</td>
<td>continuous | integer</td>
<td>4-14</td>
<td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
<td></td>
Expand Down
36 changes: 18 additions & 18 deletions docs/problems.html
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Expand Up @@ -65,17 +65,17 @@
<td>10</td>
<td>2</td>
<td></td>
<td></td>
<td></td>
<td>box</td>
<td>>=1</td>
<td></td>
<td></td>
<td>no</td>
<td></td>
<td></td>
<td></td>
<td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
<td></td>
<td>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.</td>
<td></td>
<td>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, <a href="https://doi.org/10.1145/3795101.3805368">https://doi.org/10.1145/3795101.3805368</a></td>
<td></td>
<td>unimodal</td>
<td></td>
Expand All @@ -91,7 +91,7 @@
<td></td>
<td></td>
<td></td>
<td></td>
<td>>=1</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -188,7 +188,7 @@
<td>26</td>
<td>1</td>
<td>noisy</td>
<td>box | unknown</td>
<td>unknown | box</td>
<td>>=14</td>
<td></td>
<td>noisy</td>
Expand Down Expand Up @@ -266,7 +266,7 @@
<td>fn_gasoline</td>
<td>Gasoline direct injection engine design</td>
<td>Problem</td>
<td>integer | continuous</td>
<td>continuous | integer</td>
<td>14</td>
<td>2</td>
<td>multi-fidelity</td>
Expand Down Expand Up @@ -1684,11 +1684,11 @@
<td></td>
<td>>=1</td>
<td></td>
<td>IOHexperimenter | CEC2013 reference code</td>
<td>CEC2013 reference code | IOHexperimenter</td>
<td>C++/Python</td>
<td></td>
<td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
<td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
<td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
<td>Suganthan's reference implementation | IOHprofiler experimenter framework</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -2152,11 +2152,11 @@
<td>suite_expobench</td>
<td>EXPObench</td>
<td>Suite</td>
<td>categorical | integer | continuous</td>
<td>categorical | continuous | integer</td>
<td>30-405</td>
<td>1</td>
<td>noisy</td>
<td>box | unknown</td>
<td>unknown | box</td>
<td>>=2</td>
<td></td>
<td>["observational", "real-life"]</td>
Expand Down Expand Up @@ -2219,7 +2219,7 @@
<td></td>
<td>coco-gbea</td>
<td></td>
<td>{'34 seconds', '5 seconds'}</td>
<td>{'5 seconds', '34 seconds'}</td>
<td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
<td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
<td></td>
Expand Down Expand Up @@ -2670,7 +2670,7 @@
<td></td>
<td>MECHBench</td>
<td>Python</td>
<td>{'7 minutes', '1 minute'}</td>
<td>{'1 minute', '7 minutes'}</td>
<td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
<td>Structural mechanics design optimization benchmark</td>
<td></td>
Expand Down Expand Up @@ -2873,11 +2873,11 @@
<td></td>
<td>20</td>
<td>20</td>
<td>pymoo | modact</td>
<td>modact | pymoo</td>
<td>Python</td>
<td>{'20ms'}</td>
<td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a> | <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a></td>
<td>Multi-objective optimization in Python | EPFL-LAMD modact package</td>
<td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
<td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
<td></td>
<td></td>
<td></td>
Expand Down Expand Up @@ -3013,7 +3013,7 @@
<td>suite_re</td>
<td>RE</td>
<td>Suite</td>
<td>integer | continuous</td>
<td>continuous | integer</td>
<td>4-14</td>
<td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
<td></td>
Expand Down
24 changes: 19 additions & 5 deletions problems.yaml
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down