SPEA2 - The Strength Pareto Evolutionary Algorithm 2: SPEA2 in an elitist multiobjective evolutionary algorithm. It is an improved version of the Strength Pareto EA (SPEA) and incorporates a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. SPEA2 operates with a population (archive) of fixed size, from which promising candidated are drawn as parents of the next generation. The resulting offspring then compete with the old ones for inclusion in the population

References in zbMATH (referenced in 286 articles )

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  1. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  2. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  3. Arrondo, Aránzazu Gila; Redondo, Juana L.; Fernández, José; Ortigosa, Pilar M.: Parallelization of a non-linear multi-objective optimization algorithm: application to a location problem (2015)
  4. Duarte, Abraham; Pantrigo, Juan J.; Pardo, Eduardo G.; Mladenovic, Nenad: Multi-objective variable neighborhood search: an application to combinatorial optimization problems (2015)
  5. Karshenas, Hossein; Bielza, Concha; Larrañaga, Pedro: Interval-based ranking in noisy evolutionary multi-objective optimization (2015)
  6. Michalak, Krzysztof: Using an outward selective pressure for improving the search quality of the MOEA/D algorithm (2015)
  7. Ortigosa, P.M.; Hendrix, E.M.T.; Redondo, J.L.: On heuristic bi-criterion methods for semi-obnoxious facility location (2015)
  8. Rubio-Largo, Álvaro; Vega-Rodríguez, Miguel A.; González-Álvarez, David L.: Multiobjective swarm intelligence for the traffic grooming problem (2015)
  9. Ruiz, Ana Belén; Saborido, Rubén; Luque, Mariano: A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm (2015)
  10. Xiao, Jing; Bi, Xiaojun; Wang, Kejun: A research of global ranking based many-objective optimization (2015)
  11. Xu, Gang; Yang, Yu-qun; Liu, Bin-Bin; Xu, Yi-hong; Wu, Ai-jun: An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search (2015)
  12. Zheng, Jinhua; Shen, Ruimin; Li, Miqing; Zou, Juan: An evolutionary algorithm based on information separation for many-objective optimization (2015)
  13. Assunção, Wesley Klewerton Guez; Colanzi, Thelma Elita; Vergilio, Silvia Regina; Pozo, Aurora: A multi-objective optimization approach for the integration and test order problem (2014)
  14. Chen, Yu; Zou, Xiufen: Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts (2014)
  15. Couckuyt, Ivo; Deschrijver, Dirk; Dhaene, Tom: Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization (2014)
  16. Ergul, Engin Ufuk; Eminoglu, Ilyas: DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms (2014)
  17. Fard, Hamid Mohammadi; Prodan, Radu; Fahringer, Thomas: Multi-objective list scheduling of workflow applications in distributed computing infrastructures (2014)
  18. Gacto, M.J.; Galende, M.; Alcalá, R.; Herrera, F.: $\mathrmMETSK-HD^e$: a multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems (2014)
  19. García-Alonso, Carlos R.; Pérez-Naranjo, Leonor M.; Fernández-Caballero, Juan C.: Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms (2014)
  20. Gomes, Helton Cristiano; de Assis das Neves, Francisco; Souza, Marcone Jamilson Freitas: Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations (2014)

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Further publications can be found at: http://www.tik.ee.ethz.ch/pisa/?page=bugs.php