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MOP tricks

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Reference
[1] Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection

• Some references have implied that MOEAs with small population size have difficulty in converging to the Pareto-optimal frontwith well-distributed solutions for complicated problems, especially for the MOPs with more than three objectives(Khare et al., 2003; Tan et al., 2001; Deb, 2001) [1]
  (Khare et al., 2003; Tan et al., 2001; Deb, 2001)

  • Khare, V., Yao, X., and Deb, K. (2003). Performance scaling of multi-objective evolutionary algorithms. Proceedings of the Second International Conference on Evolutionary MultiCriterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science (pp. 376–390).
  • Tan, K. C., Lee, T. H., and Khor, E. F. (2001). Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 5(6):565–588.
  • Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Chichester, UK.

MOP tricks

标签：hat   sel   pop   nsa   dynamic   difficult   spec   lin   mina   

原文地址：https://www.cnblogs.com/cloud-ken/p/12004109.html