https://github.com/thieu1995/cec2011-real-world
CEC 2011 real-world problem with Python environment
https://github.com/thieu1995/cec2011-real-world
benchmark-problem cec cec-2011 engineering-problems matlab optimization python3 real-world-problem
Last synced: 7 months ago
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CEC 2011 real-world problem with Python environment
- Host: GitHub
- URL: https://github.com/thieu1995/cec2011-real-world
- Owner: thieu1995
- License: gpl-3.0
- Created: 2022-09-30T05:48:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-19T00:04:27.000Z (over 1 year ago)
- Last Synced: 2024-09-20T00:22:28.316Z (9 months ago)
- Topics: benchmark-problem, cec, cec-2011, engineering-problems, matlab, optimization, python3, real-world-problem
- Language: MATLAB
- Homepage:
- Size: 1.01 MB
- Stars: 3
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Install environments
Install based on `requirements.txt` file. After that, you need to install the `octave` and set up the path to
the `octave-cli.exe` file.
The tested working version: `oct2py=5.6.0` and `octave=8.4.0 windows 64`.
### On Windows
```code
1. Download the octave from here: https://octave.org/download
2. Install it (default is on C:\ drive)
3. Setup the path to the octave-cli.
+ Search google how to access to: Environment Variables windows (Your version)
+ In tab User variables: click New
+ Add variable name: OCTAVE_EXECUTABLE
+ And variable value: C:\Octave\Octave-5.2.0\mingw64\bin\octave-cli.exe (This is my path, change with your path).
+ Save it and restart your computer.
```
### On Ubuntu/Linux
```code
1. Search google how to install Octave on ubuntu/linux.
2. For example:
+ https://vitux.com/how-to-install-gnu-octave-in-ubuntu/
+ https://blog.eldernode.com/install-gnu-octave-on-ubuntu-20-04/
```
# Usage
Add your Algorithms/Optimizers in current directory (in folder: cec2011-real-world/).
See the run_examples.py to know how to create the problem class and how to use it.
### Problem definition
```python
from cec2011_class import Problem01
import numpy as np
## Create object
prolem = Problem01()
## Get the ndim, lowerbound, upperbound
print(f"N dimenisons: {prolem.ndim}")
print(f"Lower bound: {prolem.lb}")
print(f"Upper bound: {prolem.ub}")
## Create a random solution
X = np.random.uniform(prolem.lb, prolem.ub)
print(f"Solution: {X}")
## Calculate the fitness value
fitness = prolem.evaluate(X)
print(f"Fitness: {fitness}")
```
### How to use with MEALPY library
```python
from cec2011_class import Problem01
from mealpy import SMA, FloatVar
prob = Problem01()
problem = {
"bounds": FloatVar(lb=prob.lb, ub=prob.ub),
"obj_func": prob.evaluate,
"minmax": "min",
"log_to": "console"
}
## Run the algorithm
model = SMA.OriginalSMA(epoch=100, pop_size=50, pr=0.03)
g_best = model.solve(problem)
print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}")
```
# Support
### Official Links
* Official source code repo: https://github.com/thieu1995/cec2011-real-world
* Official chat group: https://t.me/+fRVCJGuGJg1mNDg1
* This project also related to our another projects which are "optimization" and "machine learning", check it here:
* https://github.com/thieu1995/mealpy
* https://github.com/thieu1995/metaheuristics
* https://github.com/thieu1995/opfunu
* https://github.com/thieu1995/enoppy
* https://github.com/thieu1995/permetrics
* https://github.com/thieu1995/MetaCluster
* https://github.com/thieu1995/pfevaluator
* https://github.com/thieu1995/IntelELM
* https://github.com/thieu1995/MetaCluster
* https://github.com/thieu1995/MetaPerceptron
* https://github.com/thieu1995/EvoRBF
* https://github.com/thieu1995/reflame
* https://github.com/thieu1995/GrafoRVFL
* https://github.com/aiir-team
### Citation Request
Please include these citations if you plan to use this repository:
```code
@article{van2023mealpy,
title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author={Van Thieu, Nguyen and Mirjalili, Seyedali},
journal={Journal of Systems Architecture},
year={2023},
publisher={Elsevier},
doi={10.1016/j.sysarc.2023.102871}
}
@article{nguyen2021nqsv,
title={nQSV-Net: a novel queuing search variant for global space search and workload modeling},
author={Nguyen, Binh Minh and Hoang, Bao and Nguyen, Thieu and Nguyen, Giang},
journal={Journal of Ambient Intelligence and Humanized Computing},
volume={12},
pages={27--46},
year={2021},
publisher={Springer}
}
@article{nguyen2020hybridization,
title={Hybridization of galactic swarm and evolution whale optimization for global search problem},
author={Nguyen, Binh Minh and Tran, Trung and Nguyen, Thieu and Nguyen, Giang},
journal={IEEE Access},
volume={8},
pages={74991--75010},
year={2020},
publisher={IEEE}
}
```