主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
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中国管理科学

中国管理科学 ›› 2002, Vol. ›› Issue (6): 81-83.

• 论文 • 上一篇    下一篇

计算模糊综合评价逆问题的一种方法

金菊良1, 魏一鸣2, 付强3, 丁晶3   

  1. 1. 合肥工业大学土建学院, 安徽, 合肥, 230009;
    2. 中国科学院科技政策与管理科学研究所, 北京, 100080;
    3. 四川大学水电学院, 四川, 成都, 610065
  • 收稿日期:2002-07-01 出版日期:2002-12-28 发布日期:2012-03-06
  • 基金资助:
    国家自然科学基金资助项目(70171033);安徽省自然科学基金资助项目(00043607)

A New Scheme for Computing the Contrary Problem of Fuzzy Comprehensive Evaluation

JIN Ju-liang1, WEI Yi-ming2, FU Qiang3, DING Jing3   

  1. 1. College of Civil Engineering, Hefei University of Technology, Hefei 230009, China;
    2. Institute of Policy & Management, Chinese Academy of Sciences, Beijing 100080, China;
    3. College of Hydro-electricity, Sichuan University, Chengdu 610065, China
  • Received:2002-07-01 Online:2002-12-28 Published:2012-03-06

PDF (PC)

2128

被引次数

17

摘要： 计算模糊综合评价的逆问题,有助于总结评价经验,具有普遍的应用价值。目前常用的方法是经验性枚举选优法,其计算结果与计算者的经验和枚举的次数有关。为此,该文中把该问题等价于一个以贴近度为目标函数、权重模糊集为优化变量、含有最大最小运算的非线性优化问题,并提出用加速遗传算法(AGA)来计算该问题的新方法。实例的结果说明,AGA简便、有效且具有通用性,其计算精度高于枚举选优法的相应结果,在模糊综合评价的理论与实践中具有一定价值。

关键词: 模糊综合评价, 逆问题, 遗传算法, 优化

Abstract: It helps to summarize evaluation experiences to compute the contrary problem of fuzzy comprehensive evaluation,which possesses wide practical value.At present,the common method for solving the problem is an experiential enumerating optimization method,whose results relate to personal experience and enumerating number.Therefore,in this paper,the contrary problem is equivalent to a nonlinear optimization problem which includes maximum and minimum operation,whose objective function is the close degree function between two fuzzy sets and optimized variables are weight fuzzy set.The optimization problem can be resolved by using a scheme based on accelerating genetic algorithm developed by the authors.The result of the example analysis shows that the scheme is simple,effective and universal,and that the computation precision of the scheme is higher than one of the enumerating optimization method.The scheme takes on important value in both theory and practice of fuzzy comprehensive evaluation.

Key words: fuzzy comprehensive evaluation, contrary prolem, genetic algorithm, optimization

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