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<89919391+editorialbot@users.noreply.github.com> Date: Mon, 17 Oct 2022 13:29:21 +0100 Subject: [PATCH 2/3] Creating 10.21105.joss.02974.crossref.xml --- joss.02974/10.21105.joss.02974.crossref.xml | 267 ++++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 joss.02974/10.21105.joss.02974.crossref.xml diff --git a/joss.02974/10.21105.joss.02974.crossref.xml b/joss.02974/10.21105.joss.02974.crossref.xml new file mode 100644 index 0000000000..bb576a43d0 --- /dev/null +++ b/joss.02974/10.21105.joss.02974.crossref.xml @@ -0,0 +1,267 @@ + + + + 20221017T122855-06553bbbbac60ea20dbdb686edf13b22cda062f2 + 20221017122855 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org/ + + + + + 10 + 2022 + + + 7 + + 78 + + + + Moead-framework: a modular MOEA/D Python +framework + + + + Geoffrey + Pruvost + + + Bilel + Derbel + + + Arnaud + Liefooghe + + + + 10 + 17 + 2022 + + + 2974 + + + 10.21105/joss.02974 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.7152178 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/2974 + + + + 10.21105/joss.02974 + https://joss.theoj.org/papers/10.21105/joss.02974 + + + https://joss.theoj.org/papers/10.21105/joss.02974.pdf + + + + + + MOEA/D-DE : Multiobjective Optimization +Problems With Complicated Pareto Sets, MOEA/D and +NSGA-II + Li + IEEE Transactions on Evolutionary +Computation + 2 + 13 + 10.1109/TEVC.2008.925798 + 2009 + Li, H., & Zhang, Q. (2009). +MOEA/D-DE : Multiobjective Optimization Problems With Complicated Pareto +Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, +13(2), 284–302. +https://doi.org/10.1109/TEVC.2008.925798 + + + MOEA/d: A multiobjective evolutionary +algorithm based on decomposition + Zhang + IEEE Transactions on Evolutionary +Computation + 6 + 11 + 10.1109/TEVC.2007.892759 + 2007 + Zhang, Q., & Li, H. (2007). +MOEA/d: A multiobjective evolutionary algorithm based on decomposition. +IEEE Transactions on Evolutionary Computation, 11(6), 712–731. +https://doi.org/10.1109/TEVC.2007.892759 + + + The performance of a new version of MOEA/d on +CEC09 unconstrained MOP test instances + Zhang + 2009 IEEE congress on evolutionary +computation + 10.1109/CEC.2009.4982949 + 2009 + Zhang, Q., Liu, W., & Li, H. +(2009). The performance of a new version of MOEA/d on CEC09 +unconstrained MOP test instances. 2009 IEEE Congress on Evolutionary +Computation, 203–208. +https://doi.org/10.1109/CEC.2009.4982949 + + + Surrogate-assisted multi-objective +combinatorial optimization based on decomposition and walsh +basis + Pruvost + Proceedings of the 2020 genetic and +evolutionary computation conference + 10.1145/3377930.3390149 + 9781450371285 + 2020 + Pruvost, G., Derbel, B., Liefooghe, +A., Verel, S., & Zhang, Q. (2020). Surrogate-assisted +multi-objective combinatorial optimization based on decomposition and +walsh basis. Proceedings of the 2020 Genetic and Evolutionary +Computation Conference, 542–550. +https://doi.org/10.1145/3377930.3390149 + + + On the combined impact of population size and +sub-problem selection in MOEA/d + Pruvost + Evolutionary computation in combinatorial +optimization + 10.1007/978-3-030-43680-3_9 + 978-3-030-43680-3 + 2020 + Pruvost, G., Derbel, B., Liefooghe, +A., Li, K., & Zhang, Q. (2020). On the combined impact of population +size and sub-problem selection in MOEA/d. In L. Paquete & C. Zarges +(Eds.), Evolutionary computation in combinatorial optimization (pp. +131–147). Springer International Publishing. +https://doi.org/10.1007/978-3-030-43680-3_9 + + + The MOEADr package: A component-based +framework for multiobjective evolutionary algorithms based on +decomposition + Campelo + Journal of Statistical +Software + 6 + 92 + 10.18637/jss.v092.i06 + 1548-7660 + 2020 + Campelo, F., Batista, L. S., & +Aranha, C. (2020). The MOEADr package: A component-based framework for +multiobjective evolutionary algorithms based on decomposition. Journal +of Statistical Software, 92(6). +https://doi.org/10.18637/jss.v092.i06 + + + Pymoo: Multi-objective optimization in +python + Blank + IEEE Access + 8 + 10.1109/ACCESS.2020.2990567 + 2020 + Blank, J., & Deb, K. (2020). +Pymoo: Multi-objective optimization in python. IEEE Access, 8, +89497–89509. +https://doi.org/10.1109/ACCESS.2020.2990567 + + + A parallel global multiobjective framework +for optimization: pagmo + Biscani + Journal of Open Source +Software + 53 + 5 + 10.21105/joss.02338 + 2020 + Biscani, F., & Izzo, D. (2020). A +parallel global multiobjective framework for optimization: pagmo. +Journal of Open Source Software, 5(53), 2338. +https://doi.org/10.21105/joss.02338 + + + Redesigning the JMetal multi-objective +optimization framework + Nebro + Proceedings of the companion publication of +the 2015 annual conference on genetic and evolutionary +computation + 10.1145/2739482.2768462 + 9781450334884 + 2015 + Nebro, A. J., Durillo, J. J., & +Vergne, M. (2015). Redesigning the JMetal multi-objective optimization +framework. Proceedings of the Companion Publication of the 2015 Annual +Conference on Genetic and Evolutionary Computation, 1093–1100. +https://doi.org/10.1145/2739482.2768462 + + + Evolving the structure of evolution +strategies + van Rijn + 2016 IEEE symposium series on computational +intelligence (SSCI) + 10.1109/SSCI.2016.7850138 + 2016 + van Rijn, S., Wang, H., van Leeuwen, +M., & Bäck, T. (2016). Evolving the structure of evolution +strategies. 2016 IEEE Symposium Series on Computational Intelligence +(SSCI). +https://doi.org/10.1109/SSCI.2016.7850138 + + + DEAP: Evolutionary algorithms made +easy + Fortin + Journal of Machine Learning +Research + 13 + 2012 + Fortin, F.-A., De Rainville, F.-M., +Gardner, M.-A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary +algorithms made easy. Journal of Machine Learning Research, 13, +2171–2175. + + + + + + From e9bbf57ecd47b3f20d81e8f9e2260776fe5dfeec Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 17 Oct 2022 13:29:22 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.02974.jats --- joss.02974/10.21105.joss.02974.jats | 426 ++++++++++++++++++++++++++++ 1 file changed, 426 insertions(+) create mode 100644 joss.02974/10.21105.joss.02974.jats diff --git a/joss.02974/10.21105.joss.02974.jats b/joss.02974/10.21105.joss.02974.jats new file mode 100644 index 0000000000..4c1d19825f --- /dev/null +++ b/joss.02974/10.21105.joss.02974.jats @@ -0,0 +1,426 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +2974 +10.21105/joss.02974 + +Moead-framework: a modular MOEA/D Python +framework + + + + +Pruvost +Geoffrey + + + + + +Derbel +Bilel + + + + + +Liefooghe +Arnaud + + + + + +Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, +F-59000 Lille, France + + + + +1 +12 +2020 + +7 +78 +2974 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +optimization +Multi-objective +moead +framework + + + + + + Summary +

The Multi-Objective Evolutionary Algorithm based on Decomposition + (MOEA/D) is a general-purpose algorithm for approximating the Pareto + set of multi-objective optimization problems + (Zhang + & Li, 2007). It decomposes the original multi-objective + problem into a number of single-objective optimization sub-problems + and then uses an evolutionary process to optimize these sub-problems + simultaneously and cooperatively. MOEA/D is a state-of-the-art + algorithm in aggregation-based approaches for multi-objective + optimization.

+

The goal of the moead-framework Python package is + to provide a modular framework for scientists and researchers + interested in experimenting with MOEA/D and its numerous variants.

+
+ + Statement of Need +

The MOEA/D algorithm is now considered as a framework. MOEA/D is + the basis of many variants that improve or add new components to + improve MOEA/D performance. The first version of MOEA/D and its most + famous variants + (Li + & Zhang, 2009; + Zhang + et al., 2009) are implemented in recent multi-objective + optimization software such as pymoo + (Blank + & Deb, 2020), pygmo + (Biscani + & Izzo, 2020) and jMetal + (Nebro + et al., 2015). These implementations offer many + state-of-the-art algorithms, visualization tools or parallelization + abstraction, but they are not modular enough to test easily all MOEA/D + components. The modular R package MOEADr + (Campelo + et al., 2020) focuses on MOEA/D and allows the definition of + different variants for each component of MOEA/D. While some modular + frameworks already exist in Python for evolutionary algorithms such as + DEAP + (Fortin + et al., 2012) or ModEA + (van + Rijn et al., 2016), these do not (easily) support implementing + MOEA/D variants. Instead, they focus mostly on single-objective + optimization and CMA-ES variants respectively.

+

With the moead-framework package, we aim to + provide the modularity of the MOEADr package by using the flexibility + of Python. Indeed, we want to allow the user to update the behavior of + MOEA/D components in their research works without being limited by the + package itself. The package is focused on a modular architecture for + easily adding, updating or testing the components of MOEA/D and for + customizing how components interact with each other. Indeed, in + contrast with other existing implementations, + moead-framework does not limit the users with a + limited number of components available as parameters (8 components are + available in MOEADr). Users can easily restructure the 10 existing + components of the moead-framework and include new + ones to easily add new features without altering existing components. + Components are not only customizable with parameters as with MOEADr, + but in fact they can be added with the inheritance mechanism on the + main run() method of each algorithm.

+

For example, the moead-framework package was used + for creating novel sub-problem selection strategies and analyzing them + (Pruvost, + Derbel, Liefooghe, Li, et al., 2020), and for rewriting the + component used to generate new candidate (offspring) solutions with a + variant based on Walsh surrogates + (Pruvost, + Derbel, Liefooghe, Verel, et al., 2020).

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SoftwareCan add a new algorithmCan modify the components of the algorithms in a modular + wayCan add components to algorithms
moead-frameworkyesyesyes
MOEADryesyesno
pymooyesnono
pygmoyesnono
jMetalyesnono
+
+
+ + Documentation +

The documentation is available at the following URL: + moead-framework.github.io/framework/.

+

A + complete + example and + all + components are described in details. + Two + tutorials are made available for the user to experiment + with their own multi-objective optimization problem and to implement + their own MOEA/D variants.

+
+ + Acknowledgements +

This work was supported by the French national research agency + (ANR-16-CE23-0013-01) and the Research Grants Council of Hong Kong + (RGC Project No. A-CityU101/16).

+
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