References in zbMATH (referenced in 193 articles )

Showing results 1 to 20 of 193.
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  1. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  2. Belabid, Jabrane; Aqil, Said; Allali, Karam: Solving permutation flow shop scheduling problem with sequence-independent setup time (2020)
  3. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  4. Chen, Wentao; Han, Fei: An improved multi-objective particle swarm optimization with adaptive penalty value for feature selection (2020)
  5. Dong, Nan-jiang; Wang, Rui: MEAPCA: a multi-population evolutionary algorithm based on PCA for multi-objective optimization (2020)
  6. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  7. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  8. Filatovas, E.; Kurasova, O.; Redondo, J. L.; Fernández, J.: A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems (2020)
  9. Hamada, Naoki; Hayano, Kenta; Ichiki, Shunsuke; Kabata, Yutaro; Teramoto, Hiroshi: Topology of Pareto sets of strongly convex problems (2020)
  10. Palakonda, Vikas; Mallipeddi, Rammohan: KnEA with ensemble approach for parameter selection for many-objective optimization (2020)
  11. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  12. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  13. Vo-Duy, T.; Duong-Gia, D.; Ho-Huu, V.; Nguyen-Thoi, T.: An effective couple method for reliability-based multi-objective optimization of truss structures with static and dynamic constraints (2020)
  14. Zhang, Weiwei; Zhang, Ningjun; Wan, Hanwen; Huang, Daoying; Wen, Xiaoyu; Meng, Yinghui: Decomposition based differentiate evolution algorithm with niching strategy for multimodal multi-objective optimization (2020)
  15. Zheng, Wei; Wu, Jianyu; Zhang, Chenghu; Sun, Jianyong: A clustering-based multiobjective evolutionary algorithm for balancing exploration and exploitation (2020)
  16. Bai, Hui; Zheng, Jinhua; Yu, Guo; Yang, Shengxiang; Zou, Juan: A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation (2019)
  17. Bao, Chunteng; Xu, Lihong; Goodman, Erik D.: A novel two-archive matching-based algorithm for multi- and many-objective optimization (2019)
  18. Chabane, Brahim; Basseur, Matthieu; Hao, Jin-Kao: Lorenz dominance based algorithms to solve a practical multiobjective problem (2019)
  19. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
  20. Ermis, Gülcin; Akkan, Can: Search algorithms for improving the Pareto front in a timetabling problem with a solution network-based robustness measure (2019)

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Further publications can be found at: http://dces.essex.ac.uk/staff/zhang/webofmoead.htm