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 301 articles )

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  1. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  2. Kukkonen, Saku; Coello Coello, Carlos A.: Generalized differential evolution for numerical and evolutionary optimization (2017)
  3. Wang, Hao; Ren, Yiyi; Deutz, André; Emmerich, Michael: On steering dominated points in hypervolume indicator gradient ascent for bi-objective optimization (2017)
  4. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  5. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  6. Rohaninejad, Mohammad; Sahraeian, Rashed; Nouri, Behdin Vahedi: Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops (2016)
  7. 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)
  8. Duarte, Abraham; Pantrigo, Juan J.; Pardo, Eduardo G.; Mladenovic, Nenad: Multi-objective variable neighborhood search: an application to combinatorial optimization problems (2015)
  9. Hidalgo, Ieda G.; de Barros, Regiane S.; Fernandes, Jéssica P.T.; Estrócio, João Paulo F.; Correia, Paulo B.: Metaheuristic approaches for hydropower system scheduling (2015)
  10. Karshenas, Hossein; Bielza, Concha; Larrañaga, Pedro: Interval-based ranking in noisy evolutionary multi-objective optimization (2015)
  11. Lin, Qiuzhen; Zhu, Qingling; Huang, Peizhi; Chen, Jianyong; Ming, Zhong; Yu, Jianping: A novel hybrid multi-objective immune algorithm with adaptive differential evolution (2015)
  12. Michalak, Krzysztof: Using an outward selective pressure for improving the search quality of the MOEA/D algorithm (2015)
  13. Ortigosa, P.M.; Hendrix, E.M.T.; Redondo, J.L.: On heuristic bi-criterion methods for semi-obnoxious facility location (2015)
  14. Redondo, Juana L.; Fernández, José; Domingo Álvarez Hervás, José; Gila Arrondo, Aránzazu; Ortigosa, Pilar M.: Approximating the Pareto-front of a planar bi-objective competitive facility location and design problem (2015)
  15. Rubio-Largo, Álvaro; Vega-Rodríguez, Miguel A.; González-Álvarez, David L.: Multiobjective swarm intelligence for the traffic grooming problem (2015)
  16. Ruiz, Ana Belén; Saborido, Rubén; Luque, Mariano: A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm (2015)
  17. Wang, Honggang: Direct zigzag search for discrete multi-objective optimization (2015)
  18. Xiao, Jing; Bi, Xiaojun; Wang, Kejun: A research of global ranking based many-objective optimization (2015)
  19. 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)
  20. Zheng, Jinhua; Shen, Ruimin; Li, Miqing; Zou, Juan: An evolutionary algorithm based on information separation for many-objective optimization (2015)

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