An efficient multi-objective evolutionary algorithm: OMOEA-II An improved orthogonal multi-objective evolutionary algorithm (OMOEA), called OMOEA-II, is proposed in this paper. Two new crossovers used in OMOEA-II are orthogonal crossover and linear crossover. By using these two crossover operators, only small orthogonal array rather than large orthogonal array is needed for exploiting optimal in the global space. Such reduction in orthogonal array can avoid exponential creation of solutions of OMOEA and improve the performance in robusticity without degrading precision and distribution of solutions. Experimental results show that OMOEA-II can solve problems with high dimensions and large number of local Pareto-optimal fronts better than some existing algorithms recently reported in the literatures.

References in zbMATH (referenced in 3 articles , 1 standard article )

Showing results 1 to 3 of 3.
Sorted by year (citations)

  1. Liu, Ruochen; Chen, Yangyang; Ma, Wenping; Mu, Caihong; Jiao, Licheng: A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model (2014) ioport
  2. Goh, Chi-Keong; Tan, Kay Chen: Evolutionary multi-objective optimization in uncertain environments. Issues and algorithms (2009)
  3. Zeng, Sanyou; Yao, Shuzhen; Kang, Lishan; Liu, Yong: An efficient multi-objective evolutionary algorithm: OMOEA-II (2005)