Multi-objective Optimization Algorithm Using Cloud Model and Favour Ranking

Gao Ying; Yu Qi; Liu Wai-xi

doi:10.3969/j.issn.1007-7162.2014.03.003

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Multi-objective Optimization Algorithm Using Cloud Model and Favour Ranking

更新时间：2026-05-27

- Multi-objective Optimization Algorithm Using Cloud Model and Favour Ranking
- Vol. 31, Issue 3, Pages: 14-20(2014)
- 作者机构：
- 作者简介：
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- DOI：10.3969/j.issn.1007-7162.2014.03.003
  CLC：
- Online First：30 September 2014，
  
  Published：2014
- 稿件说明：
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高鹰, 余琦, 刘外喜. 应用云模型和Favour排序的多目标优化算法[J]. 广东工业大学学报, 2014, 31(3): 14-20.

Gao Ying, Yu Qi, Liu Wai-xi. Multi-objective Optimization Algorithm Using Cloud Model and Favour Ranking[J]. 2014, 31(3): 14-20.
高鹰, 余琦, 刘外喜. 应用云模型和Favour排序的多目标优化算法[J]. 广东工业大学学报, 2014, 31(3): 14-20. DOI： 10.3969/j.issn.1007-7162.2014.03.003.

Gao Ying, Yu Qi, Liu Wai-xi. Multi-objective Optimization Algorithm Using Cloud Model and Favour Ranking[J]. 2014, 31(3): 14-20. DOI： 10.3969/j.issn.1007-7162.2014.03.003.

摘要

论文给出了一个基于云模型和利用Favour排序的多目标优化算法，其新颖之处是依据云模型理论估计好解区域和新解的生成.该算法利用优化过程中获得的信息构建好解区域的云模型并用逆向云发生器估计该云模型的3个数字特征；之后，依据这3个数字特征，用正向云发生器生成当前子代种群，并用Favour排序对当前种群和当前子代种群的并集进行排序，然后，依据排序结果选择最好的一些个体形成下一代.该算法与其他算法就一组基准函数进行了测试比较，结果表明该算法更有效.

Abstract

A multiobjective optimization algorithm inspired from cloud model and using favour ranking is introduced. The innovation of the algorithm lies in the estimation of good solution regions and new solution production according to the cloud model theory. The algorithm used information obtained during optimization to build the cloud model for good solution regions

and estimated three digital characteristics of the cloud model by backward cloud generators. Afterwards

forward cloud generators were used to generate current offsprings population according to three digital characteristics. The population with the current population and current offsprings population was sorted using favour ranking

and the best individuals were selected to form the next population. Regarding a set of benchmark functions

the proposed algorithm was tested and compared with some other algorithms. The experimental results show that the algorithm is effective in the benchmark functions.

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