GDE3: the third evolution step of generalized differential evolution. A developed version of generalized differential evolution, GDE3, is proposed. GDE3 is an extension of differential evolution (DE) for global optimization with an arbitrary number of objectives and constraints. In the case of a problem with a single objective and without constraints GDE3 falls back to the original DE. GDE3 improves earlier GDE versions in the case of multi-objective problems by giving a better distributed solution. Performance of GDE3 is demonstrated with a set of test problems and the results are compared with other methods.

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  1. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  2. Guerrero-Peña, Elaine; Araújo, Aluízio Fausto Ribeiro: Multi-objective evolutionary algorithm with prediction in the objective space (2019)
  3. Vargas, Dênis E. C.; Lemonge, Afonso C. C.; Barbosa, Helio J. C.; Bernardino, Heder S.: Differential evolution with the adaptive penalty method for structural multi-objective optimization (2019)
  4. Duan, Qibin; Kroese, Dirk P.: Splitting for multi-objective optimization (2018)
  5. Kukkonen, Saku; Coello Coello, Carlos A.: Generalized differential evolution for numerical and evolutionary optimization (2017)
  6. Liu, Chao; Yan, Bai; Gao, Yang: A new hypervolume-based differential evolution algorithm for many-objective optimization (2017)
  7. Long, Qiang; Wu, Changzhi; Wang, Xiangyu; Jiang, Lin; Li, Jueyou: A multiobjective genetic algorithm based on a discrete selection procedure (2015)
  8. Chen, Bili; Zeng, Wenhua; Lin, Yangbin; Zhong, Qi: An enhanced differential evolution based algorithm with simulated annealing for solving multiobjective optimization problems (2014)
  9. Giagkiozis, I.; Purshouse, R. C.; Fleming, P. J.: Generalized decomposition and cross entropy methods for many-objective optimization (2014)
  10. Kotinis, Miltiadis: Improving a multi-objective differential evolution optimizer using fuzzy adaptation and (K)-medoids clustering (2014) ioport
  11. Li, Yangyang; Xu, Xia; Li, Peidao; Jiao, Licheng: Improved RM-MEDA with local learning (2014) ioport
  12. von Lücken, Christian; Barán, Benjamín; Brizuela, Carlos: A survey on multi-objective evolutionary algorithms for many-objective problems (2014)
  13. Yoon, Yourim; Kim, Yong-Hyuk: Geometricity of genetic operators for real-coded representation (2013)
  14. Qian, Feng; Xu, Bin; Qi, Rongbin; Tianfield, Huaglory: Self-adaptive differential evolution algorithm with (\alpha)-constrained-domination principle for constrained multi-objective optimization (2012) ioport
  15. Wu, Zhou; Chow, Tommy W. S.: A local multiobjective optimization algorithm using neighborhood field (2012)
  16. Yoon, Yourim; Kim, Yong-Hyuk; Moraglio, Alberto; Moon, Byung-Ro: A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation (2012)
  17. Kundu, Debarati; Suresh, Kaushik; Ghosh, Sayan; Das, Swagatam; Panigrahi, B. K.; Das, Sanjoy: Multi-objective optimization with artificial weed colonies (2011) ioport
  18. Sindhya, Karthik; Ruuska, Sauli; Haanpää, Tomi; Miettinen, Kaisa: A new hybrid mutation operator for multiobjective optimization with differential evolution (2011) ioport
  19. Durillo, J. J.; Nebro, A. J.; Luna, F.; Coello, C. A. Coello; Alba, E.: Convergence speed in multi-objective metaheuristics: efficiency criteria and empirical study (2010)
  20. Qu, B. Y.; Suganthan, P. N.: Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection (2010) ioport

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