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客观组合评价模型在水利工程方案选优中的应用 被引量:25

Objective Combined Evaluation Model for Optimizing Water Resource Engineering Schemes
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摘要  水利工程方案选优的关键是如何合理确定各评价指标的权重.为挖掘各方案评价指标样本值的整体差异信息,提出了基于理想点法和加速遗传算法的改进投影寻踪评价新方法.为挖掘各方案评价指标样本值的局部差异信息,提出了基于加速遗传算法的模糊层次分析法.综合这2种评价方法,构成了工程方案选优的客观组合评价新模型(OCEM).结果表明:OCEM挖掘评价指标样本信息比较充分,可反映各指标对综合评价结果的影响程度,计算结果更为客观、稳定、分辨率高,可在工程方案选优中推广应用. The key problem in optimizing water resource engineering schemes is how to determine with reason weights of the evaluation indexes of water resource engineering schemes. For this reason, in this paper the whole diversity information of evaluation index sample values can be mined by using projection pursuit evaluation method based on ideal solution point method and accelerating genetic algorithm (TOPSIS-PP). The local diversity information of evaluation index sample values can be mined by using improved fuzzy analytic hierarchy process evaluation method based on accelerating genetic algorithm (AGA-FAHP). And then an objective combined evaluation model (OCEM) was made through integrating TOPSIS-PP and AGA-FAHP for comprehensive evaluation of water resource engineering schemes. The research results show that the evaluation index samples information is utilized much sufficiently by using OCEM, that the decision-making information is more rich and impersonal provided by OCEM than by the common projection pursuit methods, that importance of each valuation index can be recognized in comprehensive evaluation of water resource engineering schemes, and that computation result of OCEM is objective, stability and distinguishability, so it can be widely applied to different engineering scheme optimization.
出处 《系统工程理论与实践》 EI CSCD 北大核心 2004年第12期111-116,共6页 Systems Engineering-Theory & Practice
基金 教育部优秀青年教师资助计划(教人司[2002]350) 安徽省优秀青年科技基金(2001年度) 国家自然科学基金(50099620) 安徽省自然科学基金(01045102 01045409) 四川大学高速水力学国家重点实验室开放基金(0201)
关键词 工程方案选优 组合评价 遗传算法 投影寻踪 模糊层次分析法 optimal selection of engineering schemes combined evaluation genetic algorithm projection pursuit fuzzy analytic hierarchy process
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参考文献1

  • 1Friedman J H, Turkey J W.A projection pursuit algorithm for exploratory data analysis[J].IEEE Trans on Computer, 1974,23(9):881-890.

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