A Python optimization package using Differential Evolution.
Project description
pymoode
A Python framework for Differential Evolution using pymoo (Blank & Deb, 2020).
Contents
Install / Algorithms / Survival Operators / Crowding Metrics / Usage / Citation / References / Contact / Acknowledgements
Install
First, make sure you have a Python 3 environment installed.
From PyPi:
pip install pymoode
From the current version on github:
pip install -e git+https://github.com/mooscalia/pymoode#egg=pymoode
Algorithms
- DE: Differential Evolution for single-objective problems proposed by Storn & Price (1997). Other features later implemented are also present, such as dither, jitter, selection variants, and crossover strategies. For details see Price et al. (2005).
- NSDE: Non-dominated Sorting Differential Evolution, a multi-objective algorithm that combines DE mutation and crossover operators to NSGA-II (Deb et al., 2002) survival.
- GDE3: Generalized Differential Evolution 3, a multi-objective algorithm that combines DE mutation and crossover operators to NSGA-II survival with a hybrid type survival strategy. In this algorithm, individuals might be removed in a one-to-one comparison before truncating the population by the multi-objective survival operator. It was proposed by Kukkonen, S. & Lampinen, J. (2005).
- NSDE-R: Non-dominated Sorting Differential Evolution based on Reference directions (Reddy & Dulikravich, 2019). It is an algorithm for many-objective problems that works as an extension of NSDE using NSGA-III (Deb & Jain, 2014) survival strategy.
Survival Operators
- RankSurvival: Flexible structure to implement NSGA-II rank and crowding survival with different options for crowding metric and elimination of individuals.
- ConstrainedRankSurvival: A survival operator based on rank and crowding with a special constraint handling approach proposed by Kukkonen, S. & Lampinen, J. (2005).
Crowding Metrics
- Crowding Distance ('cd'): Proposed by Deb et al. (2002) in NSGA-II. Imported from pymoo.
- Crowding Entropy ('ce'): Proposed by Wang et al. (2010) in MOSADE.
- M-Nearest Neighbors ('mnn'): Proposed by Kukkonen & Deb (2006) in an extension of GDE3 to many-objective problems.
- 2-Nearest Neighbors ('2nn'): Also proposed by Kukkonen & Deb (2006), it is a variant of M-Nearest Neighbors in which the number of neighbors is two.
Usage
For more examples, see the example notebooks single, multi, many objective problems, and a complete tutorial
import matplotlib.pyplot as plt
from pymoo.factory import get_problem
from pymoo.util.plotting import plot
from pymoo.optimize import minimize
from pymoode.nsde import NSDE
problem = get_problem("tnk")
gde3 = GDE3(pop_size=50, variant="DE/rand/1/bin", CR=0.5, F=(0.0, 0.9))
res = minimize(problem, nsde, ('n_gen', 200), save_history=True, verbose=True)
fig, ax = plt.subplots(figsize=[6, 5], dpi=100)
ax.scatter(pf[:, 0], pf[:, 1], color="navy", label="True Front")
ax.scatter(res.F[:, 0], res.F[:, 1], color="firebrick", label="GDE3")
ax.set_ylabel("$f_2$")
ax.set_xlabel("$f_1$")
ax.legend()
fig.tight_layout()
plt.show()
Citation
Please cite this library via its current ResearchGate file:
Leite, B., 2022. pymoode: Differential Evolution in Python. doi:10.13140/RG.2.2.12935.27043
References
Blank, J. & Deb, K., 2020. pymoo: Multi-Objective Optimization in Python. IEEE Access, Volume 8, pp. 89497-89509.
Deb, K. & Jain, H., 2014. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), pp. 577–601.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A. M. T., 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), pp. 182-197.
Kukkonen, S. & Deb, K., 2006. A fast and effective method for pruning of non-dominated solutions in many-objective problems. In: Parallel problem solving from nature-PPSN IX. Berlin: Springer, pp. 553-562.
Kukkonen, S. & Lampinen, J., 2005. GDE3: The third evolution step of generalized differential evolution. 2005 IEEE congress on evolutionary computation, Volume 1, pp. 443-450.
Reddy, S. R. & Dulikravich, G. S., 2019. Many-objective differential evolution optimization based on reference points: NSDE-R. Struct. Multidisc. Optim., Volume 60, pp. 1455-1473.
Price, K. V., Storn, R. M. & Lampinen, J. A., 2005. Differential Evolution: A Practical Approach to Global Optimization. 1st ed. Springer: Berlin.
Storn, R. & Price, K., 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11(4), pp. 341-359.
Wang, Y.-N., Wu, L.-H. & Yuan, X.-F., 2010. Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput., 14(3), pp. 193-209.
Contact
e-mail: bruscalia12@gmail.com
Acknowledgements
To Julian Blank, who created the amazing structure of pymoo, making such a project possible.
To Esly F. da Costa Junior, who made it possible all along, trusted in me from the start, and guided me through the path of modeling and optimization.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.