Neural Computing and Applications

, Volume 27, Issue 2, pp 495–513 | Cite as

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

  • Seyedali Mirjalili
  • Seyed Mohammad Mirjalili
  • Abdolreza Hatamlou
Original Article


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


Optimization Meta-heuristic Algorithm Benchmark Genetic Algorithm Particle Swarm Optimization Heuristic 

Supplementary material (9.1 mb)
Supplementary material 1 (ZIP 9313 kb)


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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Seyedali Mirjalili
    • 1
    • 2
  • Seyed Mohammad Mirjalili
    • 3
  • Abdolreza Hatamlou
    • 4
  1. 1.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia
  2. 2.Queensland Institute of Business and TechnologyBrisbaneAustralia
  3. 3.Zharfa Pajohesh System (ZPS) Co.TehranIran
  4. 4.Department of Computer Science, Khoy BranchIslamic Azad UniversityKhoyIran

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