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Cited by 72Metaheuristic Optimization Algorithms ResearchQuantum Computing Algorithms and ArchitectureAdvanced Multi-Objective Optimization Algorithms

The DOI matches its Crossref record, with a small difference in title or year; 4 of the 4 references it cites were found in a registry or an index.

Quantum multiverse optimization algorithm for optimization problems

Sayed, Gehad Ismail, Darwish, Ashraf, Hassanien, Aboul Ella·2019·Neural Computing and Applications
DOI 10.1007/s00521-017-3228-9
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Crossref · 10.1007/s00521-017-3228-9

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  • Under the learning axis, identifying unknowns is a key challenge because of the common assumption in deep learning that trained models will be deployed under closed-set conditions.

    “For instance, under the learning axis, -Identifying Unknowns‖ is a key challenge because of the common assumption in DL that trained models will be deployed under closed-set conditions [241].”

    From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [241]

Citations

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Research works printed but not linked: 39

  1. Abbas N, Aftan H (2014) Quantum artificial bee colony algorithm for numerical function optimization. Int J Comput Appl 93(9):28–30
  2. Coelho L (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals 37(5):1409–1418 · DOI
  3. Derrac J, Garca S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18 · DOI
  4. Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int. J. Comput. Math. 77:481–506 · DOI
  5. Draa A, Meshoul S, Talbi H (2010) Batouche a quantum-inspired differential evolution algorithm for solving the n-queens problem. Int Arab J Inf Technol 7(1):21–27
  6. Du K, Swamy M (2016) Search and optimization by metaheuristics, vol 1. Springer, New York City, p 434 · DOI
  7. Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45:322–332 · DOI
  8. Gottfried K, Yan T (2003) Quantum mechanics: fundamentals, vol. 2. Springer-Verlag, New York · DOI
  9. Han K, Kim J (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–93 · DOI
  10. Han K, Kim J (2004) Quantum-inspired evolutionary algorithms with a new termination criterion, h epsi; gate, and two-phase scheme. IEEE Trans Evol Comput 8(2):156–169 · DOI
  11. Han KH, Kim JH (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82 · DOI · Paper page
  12. Herrera B, Coelho L, Steiner M (2015) Quantum inspired particle swarm combined with Lin–Kernighan–Helsgaun method to the traveling salesman problem. Pesquisa Operacional 35:465–488 · DOI
  13. Jamil M, Yang X (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194
  14. Jangir P, Parmar S, Trivedi I, Bhesdadiya R (2017) A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng Sci Technol Int J 20(2):570–586 · DOI
  15. Jiao L, Li Y, Gong M, Zhang X (2008) Quantum-inspired immune clonal algorithm for global optimization. IEEE Trans Syst Man Cybern Part B Cybern 38(5):1234–1253 · DOI
  16. Jing L, Wenbo X, Jun S (2005) Quantum-behaved particle swarm optimization with mutation operator. In: 17th IEEE international conference on tools with artificial intelligence (ICTAI’05), pp 240–244 · DOI
  17. Jun S, Wenbo X, Bin F (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: IEEE international conference on systems, man and cybernetics, vol 4, pp 3049–3054 · DOI
  18. Kennedy J, Eberhart R(1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA, pp 1942–1948 · DOI · Paper page
  19. Layeb A (2010) Hybrid quantum scatter search algorithm for combinatorial optimization problems. J Ann Comput Sci Ser 8(2):227–244
  20. Layeb A (2013) A hybrid quantum inspired harmony search algorithm for 0–1 optimization problems. J Comput Appl Math 253:14–25 · DOI
  21. Layeb A, Saidouni DE (2004) A new quantum evolutionary local search algorithm forMAX3-SAT problem. In: Proceedings of the 3rd international workshop on hybrid artificial intelligence systems, Lecture notes in artificial intelligence, vol 5271. Springer, Berlin, pp 172–179
  22. Layeb A, Saidouni DE (2010) A new quantum evolutionary algorithm with sifting strategy for binary decision diagram ordering problem. Int J Cogn Inform Nat Intell 4(4):47–61 · DOI
  23. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: Advances in swarm intelligence: 5th international conference, ICSI, Cham, pp 86–94 · DOI
  24. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: Advances in swarm intelligence: 5th international conference, ICSI, Cham, pp 86–94 · DOI
  25. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249 · DOI
  26. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–130 · DOI
  27. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513 · DOI
  28. Mirjalili S, Seyed M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 · DOI
  29. Narayanan A, Moore M (1996) Quantum-inspired genetic algorithms. In: Proceedings of IEEE international conference on evolutionary computation, Nogaya, Japan, pp 61–66 · DOI
  30. Rehman O, Yang S, Khan S (2017) A modified quantum-based particle swarm optimization for engineering inverse problem. COMPEL Int J Comput Math Electr Electron Eng 36:168–187 · DOI
  31. Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation, vol 1, pp 325–331 · DOI
  32. Tegmark M (2004) Science and ultimate reality: quantum theory, cosmology, and complexity. Cambridge Unversity Press, Cambridge, UK, pp 459–491 · DOI
  33. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83 · DOI · Paper page
  34. Wolpert D, Macready W (2004) Quantum-inspired evolutionary algorithms with a new termination criterion, h gate, and two phase scheme. IEEE Trans Evol Comput 8(2):156–169 · DOI
  35. Yang X-S (2010) Test problems in optimization. Wiley, London, pp 261–266
  36. Yang XS, Cui ZH, Xiao RB, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation. Elsevier Science Publishers BV, Amsterdam, The Netherlands, The Netherlands, pp 3–23 · DOI
  37. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102 · DOI
  38. Ykhlef M (2011) A quantum swarm evolutionary algorithm for mining association rules in large databases. J King Saud Univ Comput Inf Sci 23:1–6 · DOI
  39. Zouache A, Moussaoui A (2015) Quantum-inspired differential evolution with particle swarm optimization for knapsack problem. J Inf Sci Eng 31:1757–1773

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Metadata

Authors
Sayed, G. I., Darwish, A., & Hassanien, A. E.
Affiliations
  • Cairo University (EG)
  • Scientific Research Group in Egypt (EG)
Corresponding author
  • Sayed, Gehad Ismail
  • Darwish, Ashraf
  • Hassanien, Aboul Ella
ORCID
Venue
Neural Computing and Applications
Publisher
Springer Science and Business Media LLC
Published
2017-11-01
Volume
31
Issue
7
Pages
2763-2780
ISSN
  • 0941-0643 (ISSN-L)
  • 1433-3058
DOI
10.1007/s00521-017-3228-9
OpenAlex
W2765693142
Cited by
72
References
4
Citations by year
2026: 5 · 2025: 10 · 2024: 8 · 2023: 13 · 2022: 9 · 2021: 10 · 2020: 7 · 2019: 4 · 2018: 6
FWCI
2.77
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Neural Computing and Applications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Keywords
quantum-inspired algorithms, hybrid metaheuristic, exploration-exploitation balance, quantum interference, benchmark function optimization, multimodal optimization

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