Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Acta Mechanica
Erschienen in: Acta Mechanica 8/2019

01.06.2019 | Original Paper

Quantum evolutionary algorithm with rotational gate and \(H_\epsilon \)-gate updating in real and integer domains for optimization

verfasst von: M. Kamalinejad, H. Arzani, A. Kaveh

Erschienen in: Acta Mechanica | Ausgabe 8/2019

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Attempts to find new advanced methods for optimization and decision-making problems in controlling real tasks have become today’s trend in mathematics, mechanics, and computer science and many other disciplines. Meta-heuristics approaches provide powerful means to find an algorithm based on nature or physical laws and phenomena such as Newtonian laws in collision (CBO), and space laws and parallel verses (MVO) and many other laws. The recently developed approach by Han and Kim (IEEE Trans. Evolut. Comput. 6(6):580–593, 2002) is based on quantum mechanics laws. A quantum evolutionary algorithm (QEA) uses Q-bit individuals in binary code analogous to genes in the conventional genetic algorithm. In different stages of iterations, the Q-bit solutions are updated using the prominent quantum gate rotational gate and \(H_\epsilon \)-gate. This paper is devoted to the assessment of the QEA using some well-known optimization problems. QEA has excellent features such as practical exploration and exploitation of domain space due to utilizing a binary approach for generation of the solutions and rotational gate updating based on the probability of 0 or 1. Here, \(H_\epsilon \)-gate and parallel phase are used to change the path of finding the optimal solution for escaping from local optima.
Vorheriger Artikel Dissipative multi-resonator acoustic metamaterials for impact force mitigation and collision energy absorption
Nächster Artikel Moving frames for Lie symmetries reduction of nonholonomic systems
Literatur
1.
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evolut. Comput. 6(6), 580–593 (2002)MathSciNetCrossRef
2.
3.
Lloyd, S.: Quantum-mechanical computers. Sc. Am. 273(4), 140–145 (1995)CrossRef
4.
5.
Church, A., Turing, A.M.: On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. 42(2s), 230–265 (1937)MathSciNetMATH
6.
Deutsch, D.: Quantum theory, the Church–Turing principle and the universal quantum computer. Proc. Lond. R. Soc. A400(1818), 97–117 (1985)MathSciNetMATHCrossRef
7.
8.
Shor, P. W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science (1994)
9.
Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Paper presented at the Evolutionary Computation, 1996. Proceedings of IEEE International Conference on Evolutionary Computation (1996)
10.
Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26(5), 1484 (1997)MathSciNetMATHCrossRef
11.
Rivest, R.L., Shamir, A., Adleman, L.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)MathSciNetMATHCrossRef
12.
Grover, L.K.: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325 (1997)CrossRef
13.
14.
Koiran, P., Nesme, V., Portier, N.: A quantum lower bound for the query complexity of Simon’s problem. In: Paper presented at the International Colloquium on Automata, Languages, and Programming (2005)
15.
Griffiths, D.: Introduction to Quantum Mechanics. Prentice Hall, Upper Saddle River (1994)
16.
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Boca Raton (2014)MATH
17.
Fogel, D.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ (1995)MATH
18.
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)MATH
19.
Haftka, R., Gurdal, Z.: Element of Structural Optimization, 3rd edn. Kluwer Academic Publisher, Dordrecht (1992)MATHCrossRef
20.
Elishakoff, I., Haftka, R., Fang, J.: Structural design under bounded uncertainty—optimization with anti-optimization. Comput. Struct. 53(6), 1401–1405 (1994)MATHCrossRef
21.
Rozvany, G., Zhou, M.: The COC algorithm, part I: cross-section optimization or sizing. Comput. Methods Appl. Mech. Eng. 89(1–3), 281–308 (1991)CrossRef
22.
Sofge, D. A.: Prospective algorithms for quantum evolutionary computation. In: Paper Presented at the Proceedings of the 2nd Quantum International Symposium (2008)
23.
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)MATH
24.
Han, K.H., Kim, J.H.: On the analysis of the quantum-inspired evolutionary algorithm with a single individual. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE, pp. 2622–2629 (2006)
25.
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithms with a new termination criterion, h-epsilon gate, and two-phase scheme. IEEE Trans. Evolut. Comput. 8, 156–169 (2004)CrossRef
26.
Ragsdell, K., Phillips, D.: Optimal design of a class of welded structures using geometric programming. J. Eng. Ind. 98(3), 1021–1025 (1976)CrossRef
27.
Deb, K.: Optimal design of a welded beam via genetic algorithms. AIAA J. 29(11), 2013–2015 (1991)CrossRef
28.
Coello, C.A.C.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41(2), 113–127 (2000)CrossRef
29.
Coello, C.A.C., Montes, E.M.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inform. 16(3), 193–203 (2002)CrossRef
30.
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20(1), 89–99 (2007)CrossRef
31.
Mezura-Montes, E., Coello, C.A.C.: An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int. J. Gen. Syst. 37(4), 443–473 (2008)MathSciNetMATHCrossRef
32.
Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213(3–4), 267–289 (2010)MATHCrossRef
33.
Kaveh, A., Mahdavi, V.R.: Colliding Bodies Optimization: Extensions and Applications. Springer, Berlin (2015)MATHCrossRef
34.
Kaveh, A.: Advances in Metaheuristic Algorithm for Optimal Design of Structures, 2nd edn. Springer International Publishing, Basel (2017)MATHCrossRef
35.
Sandgren, E.: Nonlinear integer and discrete programming in mechanical design. In: Paper Presented at the Proceeding of the ASME Design Technology Conference (1988)
36.
Kannan, B., Kramer, S.N.: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J. Mech. Des. 116(2), 405–411 (1994)CrossRef
37.
Deb, K., Gene, A.: A robust optimal design technique for mechanical component design. In: Dasgupta, D., Michalewicz, Z. (eds.) Evolutionary Algorithms in Engineering Applications, pp. 497–514. Springer, Berlin (1997)CrossRef
38.
Kaveh, A., Talatahari, S.: An improved ant colony optimization for constrained engineering design problems. Eng. Comput. 27(1), 155–182 (2010)MATHCrossRef
39.
Kaveh, A., Mahdavi, V.R.: Two-dimensional colliding bodies algorithm for optimal design of truss structures. Adv. Eng. Softw. 83, 70–79 (2015)CrossRef
40.
Gandomi, A.: Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans. 53(54), 1168–1183 (2014)CrossRef
41.
Mirjalili, S., Mirjalili, S., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2015)CrossRef
42.
Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M.: Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl. Soft Comput. 13(5), 2592–2612 (2013)CrossRef
43.
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Comput. Sci. Inform. 26, 30–45 (1996)
44.
Gandomi, A., Yang, X.-S., Alavi, A.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)CrossRef
45.
Kazemzadeh Azad, S.: Enhanced hybrid metaheuristic algorithms for optimal sizing of steel truss structures with numerous discrete variables. Struct. Multidiscip. Optim. 55, 2159–2180 (2017)CrossRef
46.
Kazemzadeh Azad, S., Hasançebi, O., Kazemzadeh, Azad S.: Upper bound strategy for metaheuristic based design optimization of steel frames. Adv. Eng. Softw. 57, 19–32 (2013)CrossRef
47.
Kaveh, A., Ilchi Ghazaan, M.: Meta-heuristic Algorithms for Optimal Design of Real-Size Structures. Springer, Basel (2018)MATHCrossRef
48.
Kazemzadeh Azad, S., Hasançebi, O.: Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique. Struct. Multidiscip. Optim. 52, 383–404 (2015)CrossRef
49.
Kazemzadeh Azad, S.: Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses. Eng. Optim. 50, 89–105 (2018)MathSciNetCrossRef
50.
Zhang, M., Luo, W., Wang, X.: Differential evolution with dynamic stochastic selection for constrained optimization. Inf. Sci. 178(15), 3043–3074 (2008)CrossRef
51.
Liu, H., Cai, Z., Wang, Y.: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl. Soft Comput. 10(2), 629–640 (2010)CrossRef
52.
Ray, T., Saini, P.: Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng. Optim. 33(6), 735–748 (2001)CrossRef
53.
Tsai, J.-F.: Global optimization of nonlinear fractional programming problems in engineering design. Eng. Optim. 37(4), 399–409 (2005)MathSciNetCrossRef
54.
Chickermane, H., Gea, H.: Structural optimization using a new local approximation method. Int. J. Numer. Methods Eng. 39(5), 829–846 (1996)MathSciNetMATHCrossRef
55.
Cheng, M.-Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)CrossRef
56.
57.
Liang, J.-J., Suganthan, P. N., Deb, K.: Novel composition test functions for numerical global optimization. In: Paper Presented at the Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS (2005)
58.
Kaveh, A., Dadras, A., Montazeran, A. H.: Chaotic enhanced colliding bodies algorithms for size optimization of truss structures. Acta Mech. Publishd Online, 1–25 (2019)
59.
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Paper Presented at the Proceedings of IEEE International Conference on Neural Networks (1995)
60.
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRef
61.
Storn, R., Price, K.J.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetMATHCrossRef
62.
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evolut. Comput. 3(2), 82–102 (1999)CrossRef
Metadaten
Titel
Quantum evolutionary algorithm with rotational gate and -gate updating in real and integer domains for optimization
verfasst von
M. Kamalinejad
H. Arzani
A. Kaveh
Publikationsdatum
01.06.2019
Verlag
Springer Vienna
Erschienen in
Acta Mechanica / Ausgabe 8/2019
Print ISSN: 0001-5970
Elektronische ISSN: 1619-6937
DOI
https://doi.org/10.1007/s00707-019-02439-2

Weitere Artikel der Ausgabe 8/2019

Acta Mechanica 8/2019 Zur Ausgabe

Original Paper

Propagation of waves at an interface between a transversely isotropic rotating thermoelastic solid half space and a fiber-reinforced magneto-thermoelastic rotating solid half space

Original Paper

3D elasticity solutions for stress field analysis of FGM circular plates subject to concentrated edge forces and couples

Original Paper

Aifantis versus Lam strain gradient models of Bishop elastic rods

Original Paper

Modeling of twinning-induced plasticity using crystal plasticity and thermodynamic framework

Original Paper

Moving frames for Lie symmetries reduction of nonholonomic systems

Original Paper

Free vibration and parametric instability of viscoelastic sandwich plates with functionally graded material constraining layer

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise