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
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