X. Ma et al., "A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables," in IEEE Transactions on Evolutionary Computation, vol. 20, no. 2, pp. 275-298, April 2016, doi: 10.1109/TEVC.2015.2455812.
的论文学习笔记,只供学习使用,不作商业用途,侵权删除。并且本人学术功底有限如果有思路不正确的地方欢迎批评指正!m-1
,而距离变量的总数是n-m+1
定义二
来学习两个决策变量之间的交互关系,算法2给出了实现细节,图5-7展示了ZDT1,DTLZ1,UF1 和UF8以及五个WFG问题的两个决策变量之间的交互关系[1] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: Wiley, 2001. [2] Q. Zhang and H. Li, “MOEA/D: A multi-objective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, Dec. 2007. [3] N. Beume, B. Naujoks, and M. Emmerich, “SMS-EMOA: Multiobjective selection based on dominated hypervolume,” Eur. J. Oper. Res., vol. 181, no. 3, pp. 1653–1669, 2007. [4] K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints,” IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577–601, Aug. 2014. [5] T. Weise, R. Chiong, and K. Tang, “Evolutionary optimization: Pitfalls and booby traps,” J. Comput. Sci. Technol., vol. 27, no. 5, pp. 907–936, 2012. [6] M. Potter and K. 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