References in zbMATH (referenced in 234 articles )

Showing results 1 to 20 of 234.
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  1. Anghinolfi, Davide; Paolucci, Massimo; Ronco, Roberto: A bi-objective heuristic approach for green identical parallel machine scheduling (2021)
  2. Liagkouras, Konstantinos; Metaxiotis, Konstantinos: Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions (2021)
  3. Li, Juan; Xin, Bin; Pardalos, Panos M.; Chen, Jie: Solving bi-objective uncertain stochastic resource allocation problems by the CVaR-based risk measure and decomposition-based multi-objective evolutionary algorithms (2021)
  4. Wen, Tao; Ge, Quanbo; Lyu, Xinan; Chen, Lei; Constantinou, Costas; Roberts, Clive; Cai, Baigen: A cost-effective wireless network migration planning method supporting high-security enabled railway data communication systems (2021)
  5. Zouache, Djaafar; Ben Abdelaziz, Fouad; Lefkir, Mira; Chalabi, Nour El-Houda: Guided moth-flame optimiser for multi-objective optimization problems (2021)
  6. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  7. Belabid, Jabrane; Aqil, Said; Allali, Karam: Solving permutation flow shop scheduling problem with sequence-independent setup time (2020)
  8. Binois, Mickael; Picheny, Victor; Taillandier, Patrick; Habbal, Abderrahmane: The Kalai-Smorodinsky solution for many-objective Bayesian optimization (2020)
  9. Boufellouh, Radhwane; Belkaid, Fayçal: Bi-objective optimization algorithms for joint production and maintenance scheduling under a global resource constraint: application to the permutation flow shop problem (2020)
  10. Chen, Hanshu; Meng, Zeng; Zhou, Huanlin: A hybrid framework of efficient multi-objective optimization of stiffened shells with imperfection (2020)
  11. Chen, Huangke; Cheng, Ran; Wen, Jinming; Li, Haifeng; Weng, Jian: Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations (2020)
  12. Chen, Wentao; Han, Fei: An improved multi-objective particle swarm optimization with adaptive penalty value for feature selection (2020)
  13. Deng, Ding-Shan; Long, Wei; Li, Yan-Yan; Shi, Xiao-Qiu: Multipopulation genetic algorithms with different interaction structures to solve flexible job-shop scheduling problems: a network science perspective (2020)
  14. Dong, Nan-jiang; Wang, Rui: MEAPCA: a multi-population evolutionary algorithm based on PCA for multi-objective optimization (2020)
  15. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  16. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  17. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  18. Filatovas, E.; Kurasova, O.; Redondo, J. L.; Fernández, J.: A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems (2020)
  19. Hamada, Naoki; Hayano, Kenta; Ichiki, Shunsuke; Kabata, Yutaro; Teramoto, Hiroshi: Topology of Pareto sets of strongly convex problems (2020)
  20. Han, Ding; Zheng, Jianrong: A Kriging model-based expensive multiobjective optimization algorithm using R2 indicator of expectation improvement (2020)

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