Nature-inspired approach: An enhanced moth swarm algorithm for global optimization

Qifang Luo, Xiao Yang and Yongquan Zhou

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 159, issue C, 57-92

Abstract: 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.

Keywords: Elite opposition-based learning; Enhanced moth swarm algorithm; Function optimization; Structure engineering design; Nature-inspired approach (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:159:y:2019:i:c:p:57-92

DOI: 10.1016/j.matcom.2018.10.011

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