计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 577-579.doi: 10.11896/j.issn.1002-137X.2017.11A.123

• 综合、交叉与应用 • 上一篇    下一篇

基于区间可信度下界的多目标优化算法研究及应用

闫红   

  1. 营口理工学院电气工程系 营口115014
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(51185319)资助

Research and Application of Multi-objective Optimization Algorithm Based on Interval Reliability Lower Bound

YAN Hong   

  • Online:2018-12-01 Published:2018-12-01

摘要: 旨在填补多目标优化算法研究的不足,以制造业中多因素耦合作用下的多目标优化问题为研究对象,首先提出区间可信度和占优关系等概念;其次基于区间可信度和占优关系建立基于区间可信度下界的多目标优化算法;最后通过多目标数值优化对所建立的优化算法进行探究。结果显示,在γ的取值相同时,H测度与进化代数呈现正相关,可以说明随着个体进化代数的增大,所提出的基于占优可信度下界的算法得到的γ-Pareto前沿越能反映真实的Pareto前沿;通过文中建立的算法与IP-MOEA和SPGA的比较可以看出,文中所建立的基于区间可信度下界的多目标优化算法与实际情况的吻合度更高,说明所建立的算法可以填补多目标优化算法的不足。

关键词: 区间可信度,占优关系,多目标优化,算法

Abstract: In order to fill the lack of research aims to multi-objective optimization algorithm,in this paper the problem of the multi-objective optimization which could be applied for manufacturing based on multiple factors was selected as the research object.Firstly,the concept of interval between credibility and dominance relations etc was proposed.Secondly,the algorithm multi-objective optimization based on the lower bound of the confidence interval was established based on confidence interval and dominance relations.Finally,the optimization algorithm was studied by multi-objective numerical optimization.The results shows that H measure and evolution are positively correlated with the increase of the individualin the same value of γ.It can explain,and the lower bound of the dominant credibility algorithm based on the -Pareto front more able to reflect the true Pareto front with the evolution algebra.It can be seen that the multi-objective optimization algorithm is more suitable for practical situation based on confidence interval bounds established compared with the proposed algorithm of IP-MOEA and SPGA.The multi-objective optimization algorithm based on the lower bound of the confidence interval can fill the lack.

Key words: Interval reliability,Dominance relation,Multi-objective optimization,Algorithm

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