Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation

doi:10.3969/j.issn.0372-2112.2020.07.015

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Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation

更新时间：2025-07-08

- Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1361-1368(2020)
- 作者机构：
  
  1. 郑州航空工业管理学院智能工程学院,河南,郑州,450046
  2. 天津工业大学机械工程学院,天津,300387
  3. 郑州航空工业管理学院航空工程学院,河南,郑州,450046
  4. 中国矿业大学信息与控制工程学院,江苏,徐州,221116
  5. 郑州航空工业管理学院智能工程学院,河南,郑州,450046
  6. 天津工业大学机械工程学院,天津,300387
  7. 郑州航空工业管理学院航空工程学院,河南,郑州,450046
  8. 中国矿业大学信息与控制工程学院,江苏,徐州,221116
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51975539, No.61876185);Aeronautical Science Foundation of China, ASFC (No.2018ZD55008);Science and Technology Key Research Program of Education Department of Henan Province (No.19A460030)
- DOI：10.3969/j.issn.0372-2112.2020.07.015
  CLC： TP301
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation[J]. Acta Electronica Sinica, 2020, 48(7): 1361-1368.
DOI：

Sloving Multidimensional Optimization Problems with Hybird Indices by Interactive Evolutionary Computation[J]. Acta Electronica Sinica, 2020, 48(7): 1361-1368. DOI： 10.3969/j.issn.0372-2112.2020.07.015.

摘要

同时存在区间显式指标和模糊隐式指标的高维混合指标优化问题是一类难以求解的不确定多目标优化问题.针对该问题，首先，分别对高维显式指标和隐式指标的主要参数按确定性多目标优化，根据获得的相关权值，将高维显式指标和高维隐式指标分别降维成一维等效区间适应值和一维等效模糊适应值，二者合成个体等效指标体；然后，依据等效指标体的占优情况，通过确定自适应参考点和偏好区域面积选择个体；最后，在大规模种群NSGA-II范式下，采用隐式指标估计策略和种群聚类方法实现交互式进化优化算法.将本文算法应用于2种混合性能指标优化问题，验证所提算法的有效性和泛化性.

Abstract

The multidimensional hybrid indices optimization problem is a kind of uncertainty multi-objective optimization problems that is difficult to solve. First

we can get relevant weights by optimizing the main parameters of explicit and implicit indices. According to these weights

multidimensional explicit indices can be reduced to an equivalent-interval fitness

and multidimensional implicit indices can be reduced to an equivalent-fuzzy fitness. Equivalent-interval fitness and equivalent-fuzzy fitness can be synthesized to an equivalent-index body. Then

we select advantage individual on the basis of equivalent-index bodies dominant situation according to adaptive reference point and preference area size. Finally

we adopt an implicit-indices estimation strategy with cluster method to realize interactive evolutionary algorithm within the framework of NSGA-II. The proposed algorithm is applied to two optimization problems with hybrid indices

and the results validate its efficiency and generalization.

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