A Framework of an Intelligent Recommendation System for Particle Swarm Optimization Based on Meta-learning

被引：0
作者：
Liu Xue-min [1 ]
Li Li [1 ]
Wang Jia [2 ]
Ge Jiao-ju [1 ]
Wang Jun [3 ]
机构：
[1] Harbin Inst Technol, Sch Econ & Management, Shenzhen 518055, Peoples R China
[2] Suzhou Vocat Inst Ind Technol, Econ & Trade Management Dept, Suzhou 215104, Peoples R China
[3] Hunan Univ Sci & Technol, Sch Business, Changsha 411201, Hunan, Peoples R China
来源：
2018 25TH ANNUAL INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING | 2018年
基金：
中国国家自然科学基金;
关键词：
Meta-learning; Recommendation system; PSOs; NETWORK;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Particle swarm optimization has shown great advantages to solve NP-hard problems due to its simplicity, intelligence, efficiency and easy enhancement. However, with a large number of particle swarm optimization variants (PSOs) proposed, there are two issues: First, are the general problems of PSOs in terms of premature convergence, universality and robustness solved thoroughly? Second, how to find the relatively appropriate PSOs in a quick and efficient way when facing real-world complex optimization problems? Therefore, it is so necessary to develop an intelligent recommendation system for PSOs to provide users a black-box tool for various application problems.
引用
收藏
页码：507 / 513
页数：7
相关论文
共 50 条
  • [21] An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection
    de Miranda, Pericles B. C.
    Prudencio, Ricardo B. C.
    de Carvalho, Andre Carlos P. L. F.
    Soares, Carlos
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2012, PT III, 2012, 7335 : 562 - 575
  • [22] A meta-learning system based on genetic algorithms
    Pellerin, E
    Pigeon, L
    Delisle, S
    DATA MINING AND KNOWLEDGE DISCOVERY: THEORY, TOOLS, AND TECHNOLOGY VI, 2004, 5433 : 65 - 73
  • [23] Meta-learning framework applied in bioinformatics inference system design
    Arredondo, Tomas
    Ormazabal, Wladimir
    INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2015, 11 (02) : 139 - 166
  • [24] A Meta-learning Framework for Bankruptcy Prediction
    Tsai, Chih-Fong
    Hsu, Yu-Feng
    JOURNAL OF FORECASTING, 2013, 32 (02) : 167 - 179
  • [25] A FEATURE REGULARIZATION BASED META-LEARNING FRAMEWORK FOR GENERALIZING PROSTATE MRI SEGMENTATION
    Wang, Hui
    Zhang, Zheng
    Zhang, Bo
    Mi, Yue
    Wu, Jingyun
    Huang, Haiwen
    Ma, Zibo
    Wang, Wendong
    2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022), 2022,
  • [26] Personalized Recommendation System Based on Meta-Learning for Human-Centric Consumer Services in Gig Economy
    Ma, Yixuan
    Wang, Zhichao
    Tang, Mincong
    Qu, Wei
    Deveci, Muhammet
    Pamucar, Dragan
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2024, 70 (01) : 1080 - 1091
  • [27] Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm
    Cai, Biao
    Zhu, Xinping
    Qin, Yangxin
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 168
  • [28] A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH
    Huo, Liangyu
    Wang, Zulin
    Xu, Mai
    Li, Yong
    Ding, Zhiguo
    Wang, Hao
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 30 (09) : 3210 - 3225
  • [29] Time series classifier recommendation by a meta-learning approach
    Abanda, A.
    Mori, U.
    Lozano, Jose A.
    PATTERN RECOGNITION, 2022, 128
  • [30] A Meta-learning Approach for Recommendation of Image Segmentation Algorithms
    Campos, Gabriel F. C.
    Barbon, Sylvio, Jr.
    Mantovani, Rafael G.
    2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 2016, : 370 - 377
12345