Amimine/Optim_Metaheuristique
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.gitignore vendored
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@@ -4,7 +4,6 @@ main.py
# UV Environment
.python-version
.venv
uv.lock
# Datasets
dataset.py

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README.md
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# Mini Projet - Optimisation Métaheuristique
Ceci est le répertoire Git du projet d'optimisation métaheuristique du groupe 9 dont les membres sont AIT MOUSSA Amine, DAANOUNI Siham et DELAMOTTE Clément. Le sujet choisi est **l'optimisation du chargement des véhicules électriques** et l'algorithme mis en place est **Multiple Objectives Particle Swarm Optimization (MOPSO) + Surrogate**. La modélisation du problème se trouvera dans le rapport.
Pour les datasets, nous avons pris diverses sources pour concevoir notre propre jeu de données:
- data/vehicle_capacity.csv: [Car Dataset (2025)](https://www.kaggle.com/datasets/abdulmalik1518/cars-datasets-2025/data)
- data/elec_prices.csv: [RTE France (éco2mix)](https://www.rte-france.com/donnees-publications/eco2mix-donnees-temps-reel/donnees-marche), les données ont été récupérées manuellement sur l'hivers 2025 (S2-S5) et l'été 2025 (S29-S32)
## Installation
Le projet a été concu à l'aide du *Python packet manager* ***UV***, il est préférable d'utiliser celui-ci pour ca facilité d'utilisation. **UV** peut être installé via le [site internet officiel](https://docs.astral.sh/uv/getting-started/installation/#installing-uv)
**Linux:**
```bash
# Installation de UV
curl -LsSf https://astral.sh/uv/install.sh | sh
```
**Windows:**
```powershell
# Installation de UV (via WinGet)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```
## Utilisation
Pour charger le projet et l'executer sans problème, il faut d'abord configurer notre environnement d'execution de la manière suivante:
```bash
# Création d'un environnement virtuel (nécessaire)
uv venv
# Téléchargement des requirements du projet
uv pip sync uv.lock
# Si uv.lock n'existe pas, vous pouvez le générer avec la commande suivante:
uv pip compile --upgrade pyproject.toml -o uv.lock
```
Enfin, vous pouvez executer n'importe quel script avec la commande `uv run main.py`

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data/elec_prices.csv Normal file
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Winter 2025; Summer 2025
13.28; 85.26
5.23; 49.28
3.52; 41.39
58.15; 40.19
40.81; 28.27
31.28; 37.05
26.95; 34.06
38.49; 31.49
88.34; 27.09
67.18; 54.14
48.02; 86.23
20.88; 117.84
12.54; 76.77
0.4; 63.01
60.01; 54.1
1158; 69.52
93.49; 94.16
71.25; 30.5
79.76; 46.2
100.64; 25.8
128.87; 20.82
107.24; 44.77
98.53; 74.46
74.23; 100.79
59.97; 74.97
59.52; 55.26
97.41; 44.71
135.01; 52.33
125.04; 75.2
97.57; 42.63
110.19; 53.6
124.05; 32.25
138.58; 39.76
124.81; 79.1
111.9; 122.84
60.38; 117.4
51.86; 104.23
13.01; 90.1
60.08; 82.12
100.99; 87.05
97.45; 105.92
94.75; 85
80.89; 70.17
109.4; 51.21
135.73; 76.81
120.36; 91.52
101.43; 121.41
86.82; 125.07
72.09; 100.97
70.07; 91.21
101.8; 78.45
140.33; 90.32
123.57; 109.39
110.01; 80
135.57; 80
144.28; 71.2
134.83; 79.4
119.79; 93.97
112; 110
96.32; 119.19
90.82; 98.8
95.46; 79.71
115.85; 49.57
114.24; 47.99
97.14; 56.62
95.74; 10
113.29; 0
123.86; 0
119.95; 18.82
113.27; 30.16
110.36; 72.92
90; 106.8
85; 51.61
90.13; 25.5
114.97; 21.83
117.77; 16.32
102.99; 3.6
95.4; 0
109.56; 0
146.66; -0.01
124; 3.08
116.73; 5.99
112.39; 43.81
104.46; 78.33
100; 58.02
127.14; 26.72
186; 17.88
128.38; 22.08
105.69; 19.41
104.91; 0
126.64; 15.23
157.07; 17.22
141.32; 8.93
121.41; 21.61
122.19; 29.33
114.53; 55.35
112.2; 81.13
138.52; 67.28
186.54; 46.63
133.76; 79.07
112.37; 76.26
116.44; 28.86
145.13; 27.67
188.31; 4.18
154; 27.05
135.31; 69.07
119.33; 113.92
118.6; 109.1
111.77; 89.79
135.56; 80.94
275; 77.83
179.56; 90.87
166.26; 96.5
143.49; 76.4
135; 61.97
221.91; 44.88
168.78; 71.63
138.77; 92.17
124.33; 111.63
114; 109.96
108.59; 96.24
130.8; 81.88
199.94; 63.27
151.9; 85.62
131.13; 103.04
115.14; 70.9
137.69; 71.05
184.32; 28
159.6; 26.53
136.02; 74.72
124.83; 87.93
118.2; 109.19
117.26; 88.69
140.8; 78.48
208.13; 51.97
149.39; 77.97
126.44; 98.35
119.51; 62.4
138.02; 59.01
179.92; 16.2
157.91; 46.49
139.97; 63.23
125.46; 85
121.06; 105
116.56; 96.27
120; 66.28
145.69; 46.4
131.7; 57.33
118.87; 41.05
117.28; 17.69
120.9; 18
167.57; 8.67
156.12; 5.98
136.4; 27.13
129.77; 65.29
123.17; 100
120.18; 60.5
121.59; 48.9
123.61; 37.92
125.43; 40.16
112.25; 32.34
116.81; 14.77
120.74; 31.66
159.68; 8.85
150.03; 16.14
131.56; 23.3
122.3; 40
116.57; 88.64
115.45; 62.29
137.33; 48.95
289; 38
236.29; 50
176.5; 63.71
182.73; 20.59
235; 21.87
473.28; 11.17
220; 14.35
153.58; 44.3
136; 60
124.35; 88.54
115.3; 50.68
138.86; 34.78
245; 22.98
200; 45
151.36; 49.45
151; 11.73
160.32; 25.92
208.44; 3.78
160.18; 22.87
137; 48.97
123.53; 57.58
106.78; 103.27
103.6; 54.44
120.57; 43.75
200; 32.55
179.06; 45.51
165; 57.38
177.03; 33.59
165.85; 24.79
159.6; 10.7
139.49; 19.89
129.86; 40.75
114.6; 77.99
105.84; 104.36
98.53; 90
113.79; 71.07
160.34; 57.75
135.21; 81.96
104.18; 91.55
95.4; 30.5
100.97; 29.01
121.78; 25.26
98.44; 30
85.09; 64.24
75.16; 107.99
48.6; 110.27
16.01; 102.22
64.93; 81
105.26; 63.98
93.63; 83.14
79.68; 100
75.07; 45.81
77.13; 31.87
99.64; 20.01
93.67; 17.42
96.72; 44.8
57.03; 87.34
25.2; 102.52
16.8; 97.8
25; 65.27
55.54; 43.33
60.73; 51.08
70.17; 37.33
55.16; 16.54
53.77; 2.95
118.32; 0
96.37; 0
86.5; 34.31
60.02; 74.53
29.7; 99.64
26.21; 80.4
44.2; 42.96
29.54; 30.02
25.95; 32.03
84.83; 6.16
14.98; -1.01
13.45; -3
51.54; -10.08
39.06; -1.49
32.35; 12.51
1 Winter 2025 Summer 2025
2 13.28 85.26
3 5.23 49.28
4 3.52 41.39
5 58.15 40.19
6 40.81 28.27
7 31.28 37.05
8 26.95 34.06
9 38.49 31.49
10 88.34 27.09
11 67.18 54.14
12 48.02 86.23
13 20.88 117.84
14 12.54 76.77
15 0.4 63.01
16 60.01 54.1
17 1158 69.52
18 93.49 94.16
19 71.25 30.5
20 79.76 46.2
21 100.64 25.8
22 128.87 20.82
23 107.24 44.77
24 98.53 74.46
25 74.23 100.79
26 59.97 74.97
27 59.52 55.26
28 97.41 44.71
29 135.01 52.33
30 125.04 75.2
31 97.57 42.63
32 110.19 53.6
33 124.05 32.25
34 138.58 39.76
35 124.81 79.1
36 111.9 122.84
37 60.38 117.4
38 51.86 104.23
39 13.01 90.1
40 60.08 82.12
41 100.99 87.05
42 97.45 105.92
43 94.75 85
44 80.89 70.17
45 109.4 51.21
46 135.73 76.81
47 120.36 91.52
48 101.43 121.41
49 86.82 125.07
50 72.09 100.97
51 70.07 91.21
52 101.8 78.45
53 140.33 90.32
54 123.57 109.39
55 110.01 80
56 135.57 80
57 144.28 71.2
58 134.83 79.4
59 119.79 93.97
60 112 110
61 96.32 119.19
62 90.82 98.8
63 95.46 79.71
64 115.85 49.57
65 114.24 47.99
66 97.14 56.62
67 95.74 10
68 113.29 0
69 123.86 0
70 119.95 18.82
71 113.27 30.16
72 110.36 72.92
73 90 106.8
74 85 51.61
75 90.13 25.5
76 114.97 21.83
77 117.77 16.32
78 102.99 3.6
79 95.4 0
80 109.56 0
81 146.66 -0.01
82 124 3.08
83 116.73 5.99
84 112.39 43.81
85 104.46 78.33
86 100 58.02
87 127.14 26.72
88 186 17.88
89 128.38 22.08
90 105.69 19.41
91 104.91 0
92 126.64 15.23
93 157.07 17.22
94 141.32 8.93
95 121.41 21.61
96 122.19 29.33
97 114.53 55.35
98 112.2 81.13
99 138.52 67.28
100 186.54 46.63
101 133.76 79.07
102 112.37 76.26
103 116.44 28.86
104 145.13 27.67
105 188.31 4.18
106 154 27.05
107 135.31 69.07
108 119.33 113.92
109 118.6 109.1
110 111.77 89.79
111 135.56 80.94
112 275 77.83
113 179.56 90.87
114 166.26 96.5
115 143.49 76.4
116 135 61.97
117 221.91 44.88
118 168.78 71.63
119 138.77 92.17
120 124.33 111.63
121 114 109.96
122 108.59 96.24
123 130.8 81.88
124 199.94 63.27
125 151.9 85.62
126 131.13 103.04
127 115.14 70.9
128 137.69 71.05
129 184.32 28
130 159.6 26.53
131 136.02 74.72
132 124.83 87.93
133 118.2 109.19
134 117.26 88.69
135 140.8 78.48
136 208.13 51.97
137 149.39 77.97
138 126.44 98.35
139 119.51 62.4
140 138.02 59.01
141 179.92 16.2
142 157.91 46.49
143 139.97 63.23
144 125.46 85
145 121.06 105
146 116.56 96.27
147 120 66.28
148 145.69 46.4
149 131.7 57.33
150 118.87 41.05
151 117.28 17.69
152 120.9 18
153 167.57 8.67
154 156.12 5.98
155 136.4 27.13
156 129.77 65.29
157 123.17 100
158 120.18 60.5
159 121.59 48.9
160 123.61 37.92
161 125.43 40.16
162 112.25 32.34
163 116.81 14.77
164 120.74 31.66
165 159.68 8.85
166 150.03 16.14
167 131.56 23.3
168 122.3 40
169 116.57 88.64
170 115.45 62.29
171 137.33 48.95
172 289 38
173 236.29 50
174 176.5 63.71
175 182.73 20.59
176 235 21.87
177 473.28 11.17
178 220 14.35
179 153.58 44.3
180 136 60
181 124.35 88.54
182 115.3 50.68
183 138.86 34.78
184 245 22.98
185 200 45
186 151.36 49.45
187 151 11.73
188 160.32 25.92
189 208.44 3.78
190 160.18 22.87
191 137 48.97
192 123.53 57.58
193 106.78 103.27
194 103.6 54.44
195 120.57 43.75
196 200 32.55
197 179.06 45.51
198 165 57.38
199 177.03 33.59
200 165.85 24.79
201 159.6 10.7
202 139.49 19.89
203 129.86 40.75
204 114.6 77.99
205 105.84 104.36
206 98.53 90
207 113.79 71.07
208 160.34 57.75
209 135.21 81.96
210 104.18 91.55
211 95.4 30.5
212 100.97 29.01
213 121.78 25.26
214 98.44 30
215 85.09 64.24
216 75.16 107.99
217 48.6 110.27
218 16.01 102.22
219 64.93 81
220 105.26 63.98
221 93.63 83.14
222 79.68 100
223 75.07 45.81
224 77.13 31.87
225 99.64 20.01
226 93.67 17.42
227 96.72 44.8
228 57.03 87.34
229 25.2 102.52
230 16.8 97.8
231 25 65.27
232 55.54 43.33
233 60.73 51.08
234 70.17 37.33
235 55.16 16.54
236 53.77 2.95
237 118.32 0
238 96.37 0
239 86.5 34.31
240 60.02 74.53
241 29.7 99.64
242 26.21 80.4
243 44.2 42.96
244 29.54 30.02
245 25.95 32.03
246 84.83 6.16
247 14.98 -1.01
248 13.45 -3
249 51.54 -10.08
250 39.06 -1.49
251 32.35 12.51

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data/vehicle_capacity.csv
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Cars Names;CC/Battery Capacity );Fuel Types
Cars Names;Battery Capacity kwh;Fuel Types
E-TRON;95;Electric
E-TRON SPORTBACK;95;Electric
I8 ROADSTER;11.6;Hybrid
@@ -94,8 +94,6 @@ Jaguar I-PACE EV400 HSE;90;Electric
Jaguar I-PACE First Edition;90;Electric
e-208;50;Electric
e-2008;50;Electric
Partner Electric;-;Electric
Expert Electric;-;Electric
Mustang Mach-E;83;Electric
F-150 Lightning;98;Electric
Transit Electric (E-Transit);89;Electric
1 Cars Names CC/Battery Capacity ) Battery Capacity kwh Fuel Types
2 E-TRON 95 Electric
3 E-TRON SPORTBACK 95 Electric
4 I8 ROADSTER 11.6 Hybrid
94 Jaguar I-PACE First Edition 90 Electric
95 e-208 50 Electric
96 e-2008 50 Electric
Partner Electric - Electric
Expert Electric - Electric
97 Mustang Mach-E 83 Electric
98 F-150 Lightning 98 Electric
99 Transit Electric (E-Transit) 89 Electric

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pyproject.toml
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@@ -1,7 +1,9 @@
[project]
name = "optim-meta"
version = "0.1.0"
description = "Add your description here"
version = "0.7.2"
description = "Metaheuristic Optimization Project"
readme = "README.md"
requires-python = ">=3.11"
dependencies = []
dependencies = [
"pandas>=2.3.3",
]

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uv.lock generated Normal file
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# This file was autogenerated by uv via the following command:
# uv pip compile pyproject.toml -o uv.lock
numpy==2.4.1
# via pandas
pandas==2.3.3
# via optim-meta (pyproject.toml)
python-dateutil==2.9.0.post0
# via pandas
pytz==2025.2
# via pandas
six==1.17.0
# via python-dateutil
tzdata==2025.3
# via pandas