References in zbMATH (referenced in 178 articles )

Showing results 1 to 20 of 178.
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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. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  3. Chen, Wentao; Han, Fei: An improved multi-objective particle swarm optimization with adaptive penalty value for feature selection (2020)
  4. Dong, Nan-jiang; Wang, Rui: MEAPCA: a multi-population evolutionary algorithm based on PCA for multi-objective optimization (2020)
  5. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  6. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  7. Filatovas, E.; Kurasova, O.; Redondo, J. L.; Fernández, J.: A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems (2020)
  8. Palakonda, Vikas; Mallipeddi, Rammohan: KnEA with ensemble approach for parameter selection for many-objective optimization (2020)
  9. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  10. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  11. 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)
  12. 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)
  13. Zheng, Wei; Wu, Jianyu; Zhang, Chenghu; Sun, Jianyong: A clustering-based multiobjective evolutionary algorithm for balancing exploration and exploitation (2020)
  14. Chabane, Brahim; Basseur, Matthieu; Hao, Jin-Kao: Lorenz dominance based algorithms to solve a practical multiobjective problem (2019)
  15. Ermis, Gülcin; Akkan, Can: Search algorithms for improving the Pareto front in a timetabling problem with a solution network-based robustness measure (2019)
  16. Eskandarpour, Majid; Ouelhadj, Djamila; Hatami, Sara; Juan, Angel A.; Khosravi, Banafsheh: Enhanced multi-directional local search for the bi-objective heterogeneous vehicle routing problem with multiple driving ranges (2019)
  17. Fang, Yilin; Liu, Quan; Li, Miqing; Laili, Yuanjun; Pham, Duc Truong: Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations (2019)
  18. Hu, Yabao; Chen, Hanning; He, Maowei; Sun, Liling; Liu, Rui; Shen, Hai: Multi-swarm multi-objective optimizer based on (p)-optimality criteria for multi-objective portfolio management (2019)
  19. Kar, Mohuya B.; Kar, Samarjit; Guo, Sini; Li, Xiang; Majumder, Saibal: A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms (2019)
  20. Liagkouras, K.; Metaxiotis, K.: Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem (2019)

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