
浏览全部资源
扫码关注微信
1.中国铁道科学研究院集团有限公司科学技术信息研究所,北京 100081
2.中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京 100190
3.中国铁道科学研究院集团有限公司通信信号研究所,北京 100081
4.高速铁路与城轨交通系统技术国家工程研究中心高速铁路行车调度实验室,北京 100081
[ "王荣笙 (1994- ),男,中国铁道科学研究院集团有限公司助理研究员,主要研究方向为进化计算、综合评价方法、人工智能、铁路运输等。" ]
[ "王晓刚(1976- ),男,博士,中国铁道科学研究院集团有限公司研究员,主要研究方向为科研管理、知识产权管理。" ]
[ "龙艺璇(1994- ),女,博士,中国铁道科学研究院集团有限公司高级工程师,主要研究方向为科技评价、科技情报分析、科学计量。" ]
[ "吕宜生(1983- ),男,博士,中国科学院自动化研究所研究员,主要研究方向为人工智能、智能网联驾驶、平行交通管理与控制系统。" ]
[ "袁志明(1980- ),男,博士,中国铁道科学研究院集团有限公司研究员,主要研究方向为铁路调度指挥、铁路信号控制。" ]
收稿日期:2024-11-11,
修回日期:2025-01-25,
纸质出版日期:2025-03-15
移动端阅览
王荣笙,王晓刚,龙艺璇等.基于改进差分进化的智能综合评分法[J].智能科学与技术学报,2025,07(01):64-76.
WANG Rongsheng,WANG Xiaogang,LONG Yixuan,et al.An intelligent comprehensive scoring approach based on improved differential evolution algorithm[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):64-76.
王荣笙,王晓刚,龙艺璇等.基于改进差分进化的智能综合评分法[J].智能科学与技术学报,2025,07(01):64-76. DOI: 10.11959/j.issn.2096-6652.202506.
WANG Rongsheng,WANG Xiaogang,LONG Yixuan,et al.An intelligent comprehensive scoring approach based on improved differential evolution algorithm[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):64-76. DOI: 10.11959/j.issn.2096-6652.202506.
针对综合评价问题中的异常评分导致的综合评分结果有失公允问题,提出基于改进差分进化的智能综合评分法,通过剔除异常评分提升综合评分结果的公平性。首先,从异常评分产生的根本原因出发,定义综合评价问题的公平性指标及目标函数,分析问题约束条件和异常评分判定条件。其次,提出基于改进差分进化的智能综合评分法,即在传统差分进化算法的框架上做出以下3点改进:提出基于评分值权数的实数编解码方式;采用比重法将评分值权数等式约束写入目标函数,将综合评价问题转化为无约束优化问题,提升算法求解效率;以公平性指标为基础,设计评分值权数优化问题知识,采用启发式种群初始化方法提升算法前期收敛速度和评分值权数结果的求解质量。最后,算例结果表明,所提出的智能综合评分法能自动判定权数较小的评分值为异常评分并予以剔除,最终在13 s内给出公平性更高的综合评分结果,有效提升了综合评分结果的公平公正性与科学合理性。
In response to the issue of biased comprehensive evaluation results caused by anomalous ratings in comprehensive evaluation problems
an intelligent comprehensive scoring approach based on improved differential evolution algorithm had been proposed
which enhanced the fairness of comprehensive evaluation results by eliminating anomalous ratings. Firstly
considering the causes of abnormal scores
the fairness index and objective function were defined for the comprehensive evaluation problem. The constraint and the criterion of abnormal scores were also analyzed. Secondly
an intelligent comprehensive scoring approach had been proposed based on improved differential evolution algorithm
which introduced the following three improvements to the traditional differential evolution algorithm framework: a real encoding and decoding method was developed based on the score weights. The weighting method was used to write the constraints of the score weights into the objective function. As a result
the comprehensive evaluation problem was transformed into an unconstrained optimization problem to improve efficiency. The problem-specific knowledge of score weights was designed based on the fairness index. A heuristic population initialization method was employed to speed up the algorithm convergence in the early stage and the solution quality of the score weights. Finally
the case study results had demonstrated that the proposed intelligent comprehensive scoring approach could automatically identify ratings with smaller weights as anomalous and eliminate them
ultimately providing a more fair comprehensive evaluation result within 13 seconds
effectively improving the fairness
impartiality
and scientific rationality of the comprehensive evaluation results.
郭亚军. 综合评价理论、方法及应用[M]. 北京: 科学出版社, 2007.
GUO Y J. Comprehensive evaluation theory、method and application[M]. Beijing: Science Publishing House Press, 2007.
陈正伟. 综合评价技术及应用[M]. 成都: 西南财经大学出版社, 2013.
CHEN Z W. Comprehensive evaluation technology and application[M]. Chengdu: Southwestern University of Finance and Economics Publishing House Press, 2013.
ROSSI P H, LIPSEY M W, FREEMAN H E. Evaluation: a systematic approach (8th edition)[M]. California: SAGE Publications, Inc, 2003.
杨晓秋, 李旭彦. 同行评议中的异常数据检测方法研究: 以科研项目评审为例[J]. 中国软科学, 2016(5): 133-142.
YANG X Q, LI X Y. Research on abnormal data detection in peer review: an example of scientific project evaluation[J]. China Soft Science, 2016(5): 133-142.
汪建, 王裴裴, 丁俊. 科技项目专家评审的元评价综合模型研究[J]. 科研管理, 2020, 41(2): 183-192.
WANG J, WANG P P, DING J. A comprehensive model on the meta-evaluation of science and technology projects by review experts[J]. Science Research Management, 2020, 41(2): 183-192.
倪苏云, 攀登, 吴冲锋. 基于遗传算法的基金绩效综合评价研究[J]. 系统工程, 2003, 21(2): 1-6.
NI S Y, PAN D, WU C F. The comprehensive evaluation of funds performance based on genetic algorithm[J]. Systems Engineering, 2003, 21(2): 1-6.
辛峻峰, 张永波, 伯佳更, 等. 基于数据驱动的遗传算法的无人艇路径规划研究[J]. 智能科学与技术学报, 2019, 1(2): 171-180.
XIN J F, ZHANG Y B, BO J G, et al. Study on path planning of unmanned surface vessel based on data-driven genetic algorithm[J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(2): 171-180.
杜宏庆, 陈德旺, 黄允浒, 等. 基于改进遗传算法与支持度的模糊系统优化建模方法[J]. 智能科学与技术学报, 2020, 2(2): 179-185.
DU H Q, CHEN D W, HUANG Y H, et al. A fuzzy system optimization modeling method based on improved genetic algorithm and support degree[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 179-185.
王荣笙, 张琦, 张涛, 等. 车站封锁下基于问题知识的高速铁路列车运行实时调整方法[J]. 中国科学: 信息科学, 2022, 52(11): 2121-2140.
WANG R S, ZHANG Q, ZHANG T, et al. Real-time rescheduling approach of train operation for high-speed railways using problem-specific knowledge under a station blockage[J]. Scientia Sinica (Informationis), 2022, 52(11): 2121-2140.
LI W, JIANG S, ZHAO Y, et al. Comprehensive evaluation and scenario simulation of water resources carrying capacity: a case study in Xiongan New Area, China[J]. Ecological Indicators, 2023, 150: 110253.
XU T, ZHANG Y J, SHI L Y, et al. A comprehensive evaluation framework of energy and resources consumption of public buildings: case study, People’s Bank of China[J]. Applied Energy, 2023, 351: 121869.
ZENG S Z, GU J X, PENG X D. Low-carbon cities comprehensive evaluation method based on Fermatean fuzzy hybrid distance measure and TOPSIS[J]. Artificial Intelligence Review, 2023, 56(8): 8591-8607.
QI Z J, LIU H X, AN Z H, et al. Comprehensive evaluation method of stope stability and its application in deep metal mine[J]. PLoS One, 2023, 18(3): e0283205.
ZHOU J, CHEN C, WANG M Z, et al. Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors[J]. International Journal of Mining Science and Technology, 2021, 31(5): 799-812.
李彬, 李旭红, 应媚. 科技计划项目评审专家精准评价模型研究: 以江苏省科技计划项目评审为例[J]. 南京师大学报(自然科学版), 2024, 47(1): 133-141.
LI B, LI X H, YING M. Research on the accurate evaluation model of experts in science and technology plan project review: taking Jiangsu Province’s science and technology plan project review as an example[J]. Journal of Nanjing Normal University (Natural Science Edition), 2024, 47(1): 133-141.
张静辉, 刘蔚, 侯春梅, 等. 基于大数据技术优化科技期刊同行评议模式研究[J]. 中国科技期刊研究, 2024, 35(1): 59-64.
ZHANG J H, LIU W, HOU C M, et al. Optimization of peer review mode of scientific journals based on big data technology[J]. Chinese Journal of Scientific and Technical Periodicals, 2024, 35(1): 59-64.
WANG R S, ZHANG Q, DAI X W, et al. An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage[J]. Applied Soft Computing, 2023, 145: 110590.
王勇, 蔡自兴, 周育人, 等. 约束优化进化算法[J]. 软件学报, 2009, 20(1): 11-29.
WANG Y, CAI Z X, ZHOU Y R, et al. Constrained optimization evolutionary algorithms[J]. Journal of Software, 2009, 20(1): 11-29.
LIANG J, BAN X X, YU K J, et al. A survey on evolutionary constrained multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2023, 27(2): 201-221.
CHEN W, PANAHI M, POURGHASEMI H R. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling[J]. Catena, 2017, 157: 310-324.
LIU H, CAI Z X, WANG Y. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization[J]. Applied Soft Computing, 2010, 10(2): 629-640.
PIOTROWSKI A P, NAPIORKOWSKI J J, PIOTROWSKA A E. Particle swarm optimization or differential evolution: a comparison[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106008.
VENKATRAMAN S, YEN G G. A generic framework for constrained optimization using genetic algorithms[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(4): 424-435.
WU P, WEI S S, WANG H, et al. Optimizing a monitoring scheme for CO 2 geological sequestration: an environmentally sustainable and cost-efficient approach[J ] . Sustainable Production and Consumption, 2023, 41: 187-200.
LI N, MA L B, YU G, et al. Survey on evolutionary deep learning: principles, algorithms, applications, and open issues[J]. ACM Computing Surveys, 2023, 56(2): 1-34.
BÄCK T H W, KONONOVA A V, VAN STEIN B, et al. Evolutionary algorithms for parameter optimization-thirty years later[J]. Evolutionary Computation, 2023, 31(2): 81-122.
0
浏览量
29
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621