Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms

GONG Dun-wei; JI Xin-fang; SUN Xiao-yan

doi:10.3969/j.issn.0372-2112.2014.01.012

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Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms

更新时间：2025-07-16

- Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms
- Acta Electronica Sinica Vol. 42, Issue 1, Pages: 77-83(2014)
- 作者机构：
  
  中国矿业大学信息与电气工程学院,江苏,徐州,221008
- 作者简介：
- 基金信息：
  
  Fundamental Research Funds for the Central Universities (No.2013XK09);National Natural Science Foundation of China (No.61105063)
- DOI：10.3969/j.issn.0372-2112.2014.01.012
  CLC： TP301
- Published：2014
- 稿件说明：
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GONG Dun-wei, JI Xin-fang, SUN Xiao-yan. Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms[J]. Acta Electronica Sinica, 2014, 42(1): 77-83.
DOI：

GONG Dun-wei, JI Xin-fang, SUN Xiao-yan. Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms[J]. Acta Electronica Sinica, 2014, 42(1): 77-83. DOI： 10.3969/j.issn.0372-2112.2014.01.012.

摘要

由于高维多目标优化问题包含的目标很多，已有的方法往往难以解决该问题.本文提出一种有效解决该问题的基于集合的进化算法，该方法以超体积、分布度，以及延展度为新的目标，将原优化问题转化为3目标优化问题；定义基于集合的Pareto占优关系，设计体现用户偏好的适应度函数；此外，还提出集合进化策略.将所提方法应用于4个基准高维多目标优化问题，并与其他2种方法比较，实验结果表明了所提方法的优越性.

Abstract

Previous methods are difficult to tackle a many-objective optimization problem since it contains many objectives.A set-based evolutionary algorithm was proposed to effectively solve the above problem in this study.In the proposed method

the original optimization problem was first transformed into a tri-objective one by taking such indicators as hyper-volume

distribution and spread as three new objectives;thereafter

a set-based Pareto dominance relation was defined

and a fitness function reflecting a user's preference was designed;additionally

set-based evolutionary strategies were suggested.The proposed method was applied to four benchmark many-objective optimization problems and compared with the other two methods.The experimental results show its advantages.

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