Abstract
This paper introduces an optimization template library (OTL), a cross-platform C++ template library for multiobjective optimization. OTL has an object-oriented architecture, which allows that different modules can be arbitrarily combined with each other. Moreover, the C++ template technique is used to increase the flexibility of OTL. Meanwhile, generic programming is widely used in OTL, and the generic algorithms can be used to process different data structures. However, compared with C++, the Python script is more suitable for building the experimental platform. To ensure that all attributes of the experimental results can be fully maintained, a database is used to store the experimental data. Moreover, batch experiments can be easily defined in a set of configuration files; thus, the experiments can be executed automatically without human intervention. In addition, serial and various parallel execution modes are supported, and the user can easily switch the running mode to distributed computing to increase the computing speed. Finally, a highly customizable data visualization tool is created to play back the data sample stored in the database. From a series of comparative studies, the accuracy and running performance of OTL are verified by the statistical results.
Original language | English |
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Title of host publication | 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1885-1892 |
Number of pages | 8 |
ISBN (Electronic) | 9781479974924 |
DOIs | |
Publication status | Published - 10 Sept 2015 |
Event | IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015 |
Publication series
Name | 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings |
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Conference
Conference | IEEE Congress on Evolutionary Computation, CEC 2015 |
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Country/Territory | Japan |
City | Sendai |
Period | 25/05/15 → 28/05/15 |
Bibliographical note
Funding Information:The authors wish to thank the support of the Hunan Provincial Innovation Foundation For Postgraduate (Grant No. CX2013A011), the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), and the Construct Program of the Key Discipline in Hunan Province.
Publisher Copyright:
© 2015 IEEE.
Keywords
- evolutionary multi-objective optimization
- generic programming
- objectoriented architecture
ASJC Scopus subject areas
- Computer Science Applications
- Computational Mathematics