期刊
MATHEMATICS AND COMPUTERS IN SIMULATION
卷 159, 期 -, 页码 57-92出版社
ELSEVIER
DOI: 10.1016/j.matcom.2018.10.011
关键词
Elite opposition-based learning; Enhanced moth swarm algorithm; Function optimization; Structure engineering design; Nature-inspired approach
类别
资金
- National Science Foundation of China [61563008, 61463007]
- Project of Guangxi University for Nationalities Science Foundation, China [2016GXNSFAA380264, 2018GXNSFAA138146]
- Edanz Group China
The moth swarm algorithm (MSA) is a recent swarm intelligence optimization algorithm, but its convergence precision and ability can be limited in some applications. To enhance the MSA's exploration abilities, an enhanced MSA called the elite opposition-based MSA (EOMSA) is proposed. For the EOMSA, an elite opposition-based strategy is used to enhance the diversity of the population and its exploration ability. The EOMSA was validated using 23 benchmark functions and three structure engineering design problems. The results show that the EOMSA can find a more accurate solution than other population-based algorithms, and it also has a fast convergence speed and high degree of stability. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.Y. All rights reserved.
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