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.
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.
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/
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.
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}")
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}")-
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
Please include these citations if you plan to use this repository:
@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}
}