This framework is used by the following papers :
Title |
Journal / Conference |
|---|---|
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D [PDL+20a] |
EvoCOP 2020 |
Surrogate-assisted Multi-objective Combinatorial Optimization based on Decomposition and Walsh Basis [PDL+20b] |
GECCO 2020 |
References¶
- BI20
Francesco Biscani and Dario Izzo. A parallel global multiobjective framework for optimization: pagmo. Journal of Open Source Software, 5(53):2338, 2020. URL: https://doi.org/10.21105/joss.02338, doi:10.21105/joss.02338.
- CBA20
Felipe Campelo, Lucas S. Batista, and Claus Aranha. The moeadr package: a component-based framework for multiobjective evolutionary algorithms based on decomposition. Journal of Statistical Software, 2020. URL: http://dx.doi.org/10.18637/jss.v092.i06, doi:10.18637/jss.v092.i06.
- LZ09
H. Li and Q. Zhang. MOEA/D-DE : Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2):284–302, April 2009. doi:10.1109/TEVC.2008.925798.
- LVH14
Arnaud Liefooghe, Sébastien Verel, and Jin-Kao Hao. A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Applied Soft Computing, 16:10–19, 2014. URL: https://hal.archives-ouvertes.fr/hal-00801793, doi:10.1016/j.asoc.2013.11.008.
- Lus
Thibaut Lust. Multiobjective knapsack problem. URL: http://www-desir.lip6.fr/~lustt/Research.html#MOKP.
- NDV15
Antonio J. Nebro, Juan J. Durillo, and Matthieu Vergne. Redesigning the jmetal multi-objective optimization framework. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion ‘15, 1093–1100. New York, NY, USA, 2015. Association for Computing Machinery. URL: https://doi.org/10.1145/2739482.2768462, doi:10.1145/2739482.2768462.
- PDL+20a
Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, and Qingfu Zhang. On the combined impact of population size and sub-problem selection in moea/d. In Luís Paquete and Christine Zarges, editors, Evolutionary Computation in Combinatorial Optimization, 131–147. Cham, 2020. Springer International Publishing. doi:10.1007/978-3-030-43680-3_9.
- PDL+20b
Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Sebastien Verel, and Qingfu Zhang. Surrogate-assisted multi-objective combinatorial optimization based on decomposition and walsh basis. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO ’20, 542–550. New York, NY, USA, 2020. Association for Computing Machinery. URL: https://doi.org/10.1145/3377930.3390149, doi:10.1145/3377930.3390149.
- VLJD11
Sébastien Verel, Arnaud Liefooghe, Laetitia Jourdan, and Clarisse Dhaenens. Analyzing the effect of objective correlation on the efficient set of mnk-landscapes. In Carlos A. Coello Coello, editor, Learning and Intelligent Optimization, 116–130. Berlin, Heidelberg, 2011. Springer Berlin Heidelberg.
- ZDT00
Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput., 8(2):173–195, June 2000. URL: https://doi.org/10.1162/106365600568202, doi:10.1162/106365600568202.
- BlankDeb20
J. Blank and K. Deb. Pymoo: multi-objective optimization in python. IEEE Access, 8():89497–89509, 2020. doi:10.1109/ACCESS.2020.2990567.
- ZhangLiuLi09
Q. Zhang, W. Liu, and H. Li. The performance of a new version of moea/d on cec09 unconstrained mop test instances. In 2009 IEEE Congress on Evolutionary Computation, volume, 203–208. 2009. doi:10.1109/CEC.2009.4982949.
- ZhangLi07
Q. Zhang and H. Li. Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712–731, 2007. doi:10.1109/TEVC.2007.892759.