Journal
APPLIED SOFT COMPUTING
Volume 69, Issue -, Pages 806-815Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2017.06.029
Keywords
Firefly algorithm (FA); Multi-objective firefly algorithm; Multi-objective optimization; Big data optimization
Categories
Funding
- National Natural Science Foundation of China [61663028, 51669014, 61402294]
- Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing [2016WICSIP015]
- Natural Science Foundation of Jiangxi Province [20171BAB202035]
- China Scholarship Council [201608360029]
- Major Fundamental Research Project in the Science and Technology Plan of Shenzhen [JCYJ20160310095523765, JCYJ20140828163633977]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
- Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)
Ask authors/readers for more resources
Multi-objective evolutionary algorithms (MOEAs) have shown good performance on many benchmark and real world multi-objective optimization problems. However, MOEAs may suffer from some difficulties when solving big data optimization problems with thousands of variables. Firefly algorithm (FA) is a new meta-heuristic, which has been proved to be a good optimization tool. In this paper, we present a hybrid multi-objective FA (HMOFA) for big data optimization. A set of big data optimization problems, including six single objective problems and six multi-objective problems, are tested in the experiments. Computational results show that HMOFA achieves promising performance on all test problems. (C) 2017 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
