Teaching-Learning-Based Optimization (TLBO) algorithm
Overview
The TLBO algorithm is a population-based optimization technique inspired by the teaching-learning process. This package provides an easy-to-use implementation of TLBO for optimizing various objective functions.
Features
Simple and intuitive interface for using TLBO
Supports multiple objective functions
Customizable parameters such as population size, number of dimensions, and bounds
Efficient optimization for various types of problems
Installation
You can install the package using pip:
pip install tlbo-optimization
Example
Here is an example of how to use the TLBO algorithm:
from tlbo_optimization.tlbo_optimization import TLBO
def objective_function(x):
return sum(x ** 2)
tlbo = TLBO(
population_size=30,
dimensions=5,
lower_bound=-10,
upper_bound=10,
max_iter=100,
obj_func=objective_function
)
best_solution, best_fitness = tlbo.optimize()
print(f"Best solution: {best_solution}")
print(f"Best fitness: {best_fitness}")
Contributing
Contributions are welcome! Please read the CONTRIBUTING.rst for details on how to contribute.
License
This project is licensed under the GNU General Public License v3.
Release files for tlbo-optimization 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tlbo_optimization-0.1.0.tar.gz | 10.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tlbo_optimization-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:15.2 kB
Release files / tlbo_optimization-0.1.0.tar.gz
| Download URL | tlbo_optimization-0.1.0.tar.gz |
|---|---|
| Size | 10.5 kB |
| Tags | Source |
|
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Release files / tlbo_optimization-0.1.0-py3-none-any.whl
| Download URL | tlbo_optimization-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.8 kB |
| Tags | Python 3 |
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