Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design

ZHANG Yi; WAN Xing-yu; ZHENG Xiao-dong; SUN Li-li

doi:10.3969/j.issn.0372-2112.2016.01.013

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Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design

更新时间：2025-07-16

- Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design
- Acta Electronica Sinica Vol. 44, Issue 1, Pages: 87-94(2016)
- 作者机构：
  
  三峡大学机械与动力学院,湖北,宜昌,443002
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51275274);Postgraduate Research Innovation Fund of China Three Gorges University (No.2013CX029)
- DOI：10.3969/j.issn.0372-2112.2016.01.013
  CLC： TP18
- Published：2016
- 稿件说明：
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[1]张屹,万兴余,郑小东,孙莉莉.基于正交设计的元胞多目标遗传算法[J].电子学报,2016,44(01):87-94.

ZHANG Yi, WAN Xing-yu, ZHENG Xiao-dong, et al. Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design[J]. Acta Electronica Sinica, 2016, 44(1): 87-94.
[1]张屹,万兴余,郑小东,孙莉莉.基于正交设计的元胞多目标遗传算法[J].电子学报,2016,44(01):87-94. DOI： 10.3969/j.issn.0372-2112.2016.01.013.

ZHANG Yi, WAN Xing-yu, ZHENG Xiao-dong, et al. Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design[J]. Acta Electronica Sinica, 2016, 44(1): 87-94. DOI： 10.3969/j.issn.0372-2112.2016.01.013.

摘要

元胞多目标遗传算法在求解两目标优化问题时是比较高效的.但是

初步实验显示其在求解三目标优化问题(例如DTLZ系列)时

表现不是十分令人满意.为了进一步提高算法的性能

引入了正交设计的思想

提出了基于正交设计的多目标元胞遗传算法.在改进算法的迭代过程中

先对父代个体进行分段

之后按照正交表来对这些片段进行重新组合产生多个子代个体

然后从这些子代个体中找出适应度较优的进入下一代种群.实验结果表明

引入正交设计思想能够提高算法性能

与其他优秀算法进行比较的结果说明

改进算法求解三目标问题(DTLZ系列)也是具有竞争力的.

Abstract

Multi-objective cellular genetic algorithm has proven to be effective in solving bi-objective MOPs.However

preliminary experiments have revealed that it has difficulties when dealing with three-objective MOPs(the DTLZ problem family).In order to enhance the performance

the orthogonal design idea is introduced and a new cellular genetic algorithm called cellular genetic algorithm for multi-objective optimization based on orthogonal design is proposed.In the progress of iteration of improved algorithm

the parent individuals are divided into many segments

and then several offsprings are produced by recombining the segments according to the orthogonal table

finally

choose the individuals which have better fitness value from the offsprings to the next population.The experiments show that the performance is improved after introducing the orthogonal design.Compared with several state-of-the-art multi-objective metaheuristics

the obtained results show that the improved algorithm is competitive for DTLZ problem family

too.

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Keywords

references

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