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  • Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

    Author(s)
    Mirjalili, Seyedali
    Mirjalili, Seyed Mohammad
    Hatamlou, Abdolreza
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2016
    Metadata
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    Abstract
    This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic ...
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    This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.
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    Journal Title
    Neural Computing and Applications
    DOI
    https://doi.org/10.1007/s00521-015-1870-7
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial intelligence not elsewhere classified
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/101356
    Collection
    • Journal articles

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