文振华1,
郝国生2
1.郑州航空工业管理学院机电工程学院 ??郑州 ??450046
2.江苏师范大学计算机科学与技术学院 ??徐州 ??221116
基金项目:国家自然科学基金(61673196)，河南省科技攻关项目(172102210513)，河南省高等学校重点科研项目(18A120012)
详细信息
作者简介:郭广颂：男，1978年生，副教授，主要从事智能控制与进化优化方面研究
文振华：男，1976年生，副教授，主要从事航空发动机状态监测与故障诊断研究
郝国生：男，1972年生，副教授，主要从事进化优化方面研究
通讯作者:郭广颂 guogs78@126.com
中图分类号:TP301计量
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被引次数:0
出版历程
收稿日期:2017-12-28
修回日期:2018-05-16
网络出版日期:2018-07-12
刊出日期:2018-09-01
Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration
Guangsong GUO1,,,Zhenhua WEN1,
Guosheng HAO2
1. School of Mechatronics Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
2. College of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China
Funds:The National Natural Science Foundation of China (61673196), The Science and Technology Research Project of Henan Province (172102210513), The Key Scientific Research Project in Colleges and Universities of Henan Province (18A120012)
摘要
摘要:采用交互式遗传算法求解大数据信息检索问题时，为实现偏好信息的提取和优化，单用户需完成较多数量的人-机交互操作，由此易产生用户疲劳、算法搜索效率低的难题。对此，该文在算法中引入多用户并行策略，通过群体决策优势，提高样本利用效率。首先，根据优化目标性质确定共性化协同或个性化协同类型，基于用户浏览行为计算用户相似度和个体相似度。然后，通过共享偏好相似用户的偏好相似个体预测个体区间适应值。基于个体表现型相似度聚类，提出大规模种群个体“区间数-区间数”适应值赋值策略。最后，依据子代种群个体与父代种群最优个体的相似性，推荐用户最佳评价个体。将所提方法应用于装饰性墙壁纸选型问题，并与已有典型方法比较。结果表明，所提方法在推荐个体质量、减轻用户疲劳、提高搜索效率等方面均具有优越性。
关键词:遗传算法/
交互/
群体决策/
多用户/
协同
Abstract:When using interactive genetic algorithm to solve big data information retrieval problem, single user needs to complete more human-machine interactive operation to achieve preference information extraction and optimization, thus it is easy to generate the problem of user fatigue and algorithm low efficiency. A multi-user strategy is introduced by making full use of the advantages of group decision to improve the sample utilization efficiency. First of all, multi-user collaborative type is devided into common collaboration or personalized collaboration according to the optimization goal which calculats user similarity and individual similarity based on user’s browsing behaviors. Then, individuals’ interval fitness is forecasted by sharing similar individual of similarity users. Based on phenotype similarity clustering, the large scale population individuals of " interval-interval” fitness assignment strategy is introduced. Finally, the best evaluation individual is recommended according to the similarities between offspring individuals and parent individuals. The proposed method is applied to decorative wallpaper design problem and is compared with existing typical methods. The experimental results confirm that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.
Key words:Genetic algorithms/
Interaction/
Collective decision making/
Multi-user/
Collaboration
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