pymoode

A Python framework for Differential Evolution using pymoo (Blank & Deb, 2020).

Read the Docs

Contents

Install | Algorithms | Survival Operators | Crowding Metrics | Usage | Structure | 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 git+https://github.com/mooscaliaproject/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). Variants with M-Nearest Neighbors and 2-Nearest Neighbors survival are also available.
• 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

• RandAndCrowding: Flexible structure to implement NSGA-II rank and crowding survival with different options for crowding metric and elimination of individuals.
• ConstrRankAndCrowding: 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.
• Pruning Crowding Distance ('pruning-cd' or 'pcd'): Proposed by Kukkonen & Deb (2006a), it recursively recalculates crowding distances as removes individuals from a population to improve diversity.
• M-Nearest Neighbors ('mnn'): Proposed by Kukkonen & Deb (2006b) in an extension of GDE3 to many-objective problems.
• 2-Nearest Neighbors ('2nn'): Also proposed by Kukkonen & Deb (2006b), it is a variant of M-Nearest Neighbors in which the number of neighbors is two.
• Crowding Entropy ('ce'): Proposed by Wang et al. (2010) in MOSADE.

Metrics 'pcd', 'mnn', and '2nn' are recursively recalculated as individuals are removed, to improve the population diversity. Therefore, they are implemented using cython to reduce computational time. If compilation fails, .py files are used instead, which makes it slightly slower.

Usage

For more examples, read the docs

import matplotlib.pyplot as plt
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoode.algorithms import GDE3
from pymoode.survival import RankAndCrowding

problem = get_problem("tnk")
pf = problem.pareto_front()

gde3 = GDE3(
    pop_size=50, variant="DE/rand/1/bin", CR=0.5, F=(0.0, 0.9),
    survival=RankAndCrowding(crowding_func="pcd")
)
    
res = minimize(problem, gde3, ('n_gen', 200), seed=12)

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()

Alternatively, on the three-objective problem DTLZ2, it would produce amazing results.

problem = get_problem("dtlz2")

gde3mnn = GDE3(
    pop_size=150, variant="DE/rand/1/bin", CR=0.5, F=(0.0, 0.9),
    survival=RankAndCrowding(crowding_func="mnn")
)
    
res = minimize(problem, gde3mnn, ('n_gen', 250), seed=12)

Structure

pymoode
├───algorithms
│   ├───DE
│   ├───GDE3
│   ├───NSDE
│   └───NSDER
├───survival
│   ├───RankAndCrowding
│   └───ConstrRankAndCrowding
├───performance
│   └───SpacingIndicator
└───operators
    ├───dem.py
    │   └───DEM
    ├───dex.py
    │   └───DEX
    └───des.py
        └───DES

Citation

This package was developed as part of an academic optimization project. Please, if you use it for research purposes, cite it using the published article:

Leite, B., Costa, A. O. S., Costa, E. F., 2023. Multi-objective optimization of adiabatic styrene reactors using Generalized Differential Evolution 3 (GDE3). Chem. Eng. Sci., Volume 265, Article 118196. doi:10.1016/j.ces.2022.118196.

References

Blank, J. & Deb, K., 2020. pymoo: Multi-Objective Optimization in Python. IEEE Access, Volume 8, pp. 89497-89509. doi:10.1109/ACCESS.2020.2990567.

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. doi:10.1109/TEVC.2013.2281535.

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. doi:10.1109/4235.996017.

Kukkonen, S. & Deb, K., 2006a. Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems. Vancouver, s.n., pp. 1179-1186. doi:10.1109/CEC.2006.1688443.

Kukkonen, S. & Deb, K., 2006b. 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. doi:10.1007/11844297_56.

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. doi:10.1109/CEC.2005.1554717.

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. doi:10.1007/s00158-019-02272-0.

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. doi:10.1023/A:1008202821328

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. doi:10.1007/s00500-008-0394-9

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, for the unconditional support all along.