References in zbMATH (referenced in 71 articles )

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  1. Puertas-Martín, S.; Redondo, J. L.; Ferrández, M. R.; Pérez-Sánchez, H.; Ortigosa, P. M.: MultiPharm-DT: a multi-objective decision tool for ligand-based virtual screening problems (2022)
  2. Sánchez-Oro, Jesús; López-Sánchez, Ana D.; Colmenar, J. Manuel: A multi-objective parallel variable neighborhood search for the bi-objective obnoxious p-median problem (2022)
  3. Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García-Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.: Metaheuristics “In the large” (2022)
  4. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  5. López-Sánchez, A. D.; Sánchez-Oro, J.; Laguna, M.: A new scatter search design for multiobjective combinatorial optimization with an application to facility location (2021)
  6. Yan, Zeyuan; Tan, Yanyan; Zheng, Wei; Meng, Lili; Zhang, Huaxiang: Leader recommend operators selection strategy for a multiobjective evolutionary algorithm based on decomposition (2021)
  7. Chou, Jui-Sheng; Truong, Dinh-Nhat: Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems (2020)
  8. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  9. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  10. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  11. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  12. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  13. Ruiz, Ana B.; Saborido, Rubén; Bermúdez, José D.; Luque, Mariano; Vercher, Enriqueta: Preference-based evolutionary multi-objective optimization for portfolio selection: a new credibilistic model under investor preferences (2020)
  14. Stanojević, Bogdana; Glover, Fred: A new approach to generate pattern-efficient sets of non-dominated vectors for multi-objective optimization (2020)
  15. Tang, Weisen; Liu, Hai-Lin; Chen, Lei; Tan, Kay Chen; Cheung, Yiu-ming: Fast hypervolume approximation scheme based on a segmentation strategy (2020)
  16. Ying, Weiqin; Huang, Junjie; Wu, Yu; Deng, Yali; Xie, Yuehong; Wang, Zhenyu; Lin, Zhiyi: Multi-dimensional tree guided efficient global association for decomposition-based evolutionary many-objective optimization (2020)
  17. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  18. Bock, Stefan; Klamroth, Kathrin: Combining traveling salesman and traveling repairman problems: a multi-objective approach based on multiple scenarios (2019)
  19. Ioannis T. Christou: Popt4jlib: A Parallel/Distributed Optimization Library for Java (2019) arXiv
  20. Majumder, Saibal; Kar, Samarjit; Pal, Tandra: Uncertain multi-objective Chinese postman problem (2019)

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