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

页码：507 / 513

页数：7

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