Top

Published in:

2019 | OriginalPaper | Chapter

Performance of Static Spatial Topologies in Fine-Grained QEA on a P-PEAKS Problem Instance

Authors : Nija Mani, Gur Saran, Ashish Mani

Published in: System Performance and Management Analytics

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Population-based meta-heuristics can admit population models and neighborhood topologies, which have a significant influence on their performance. Quantum-inspired evolutionary algorithms (QEA) often use coarse-grained population model and have been successful in solving difficult search and optimization problems. However, it was recently shown that the performance of QEA can be improved by changing its population model and neighborhood topologies. This paper investigates the effect of static spatial topologies on the performance of QEA with fine-grained population model on well-known benchmark problem generator known as P-PEAKS.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Prediction of El-Nino Year and Performance Analysis on the Calculated Correlation Coefficients

next chapter Android Malware Detection Using Code Graphs

Narayanan, A., & Moore, M. (1996). Quantum-inspired genetic algorithms In Proceedings of IEEE International Conference on Evolutionary Computation, pp. 61–66, 20–22 May 2016.

Michalewicz, Z., & Fogel, D. B. (2004). How to solve it: Modern heuristics. Springer.

Xing, H., Xu, L., Qu, R., & Qu, Z. (2016). A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding. In 2016 16th International Symposium on Communications and Information Technologies (ISCIT), Qingdao (pp. 186–190).

Manikanta, G., Mani, A., Singh, H. P. & Chaturvedi, D. K. (2016). Placing distributed generators in distribution system using adaptive quantum inspired evolutionary algorithm. In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 157–162), Kolkata.

Patvardhan, C., Bansal, S., Srivastav, A. (2016, February). Parallel improved quantum inspired evolutionary algorithm to solve large size quadratic knapsack problems. Swarm Evol Comput, 26, 175–190. ISSN 2210-6502.

da Silveira, L. R., Tanscheit, R., Vellasco, M. M. B. R. (2017, January). Quantum inspired evolutionary algorithm for ordering problems. Expert Syst Appl, 67, 71–83. ISSN- 0957-4174.

Yu, G. R., Huang, Y. C., & Cheng, C. Y. (2016). Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm. International Journal of Systems Science, 47(9), 2225–2236.CrossRef

Pavithr, R. S., & Gursaran. (2016, August). Quantum inspired social evolution (QSE) algorithm for 0–1 knapsack problem. Swarm Evol Comput, 29, 33–46. ISSN 2210-6502.

Patvardhan, C., Narain, A., & Srivastava, A. (2007, December). Enhanced quantum evolutionary algorithm for difficult knapsack problems. In Proceedings of International Conference on Pattern Recognition and Machine Intelligence, Lecture Notes in Computer Science. Kolkata: Springer.

10.

Platelt, M. D., Schliebs, S., & Kasabov, N. (2007). A versatile quantum inspired evolutionary algorithm. Proceedings of IEEE CEC, 2007, 423–430.

11.

Mani, N., Gursaran, Sinha, A. K., & Mani, A. (2012). An evaluation of cellular population model for improving QiEA. In Proceedings of GECCO-2012.

12.

Han, K. H., & Kim, J. H. (2002). Quantum–inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 6(6), 580–593.CrossRef

13.

Han, K. H., & Kim, J. H. (2004). Quantum-inspired evolutionary algorithms with a new termination criterion, H gate and two phase scheme. IEEE Trans Evol Comput, 8(2), 156–168.

14.

Mani, N., Gursaran, Sinha, A. K., & Mani, A. (2014). Effect of population structures on quantum-inspired evolutionary algorithm. Appl Comput Intell Soft Comput, 2014(2014), 22 p. Article ID 976202.

15.

Mani, N., Gursaran, & Mani, A. (2015). Performance of static random topologies in fine-grained QEA on P-PEAKS problem instances. In 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 163–168), Kolkata. https://doi.org/10.1109/icrcicn.2015.7434229.

16.

Alba, E., & Dorronsoro, B. (2005). The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput, 8(2), 126–142.

17.

Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In Proceeding of the 2002 Congress on Evolutionary Computation, Honolulu, Hawali, 12–17 May 2002.

18.

Lane, J., Engelbrecht, A., & Gain, J. (2008, September). Particle swarm optimization with spatially meaningful neighbours. Swarm Intell Symp. SIS 2008. IEEE pp. 1, 8, 21–23. https://doi.org/10.1109/sis.2008.4668281.

19.

Derrac, J., Garcia, 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.CrossRef

20.

Aczel, A. D., & Sounderpandian, J. (2006). Complete business statistics. Boston, Mass: McGraw-Hill/Irwin.

21.

Mani, N., Gursaran, & Mani, A. (2016). Design of cellular quantum-inspired evolutionary algorithms with random topologies. Quantum Inspired Comput Intell Res Appl, 111–146.

Title: Performance of Static Spatial Topologies in Fine-Grained QEA on a P-PEAKS Problem Instance
Authors: Nija Mani
Gur Saran
Ashish Mani
Publisher: Springer Singapore
Book: System Performance and Management Analytics
Print ISBN: 978-981-10-7322-9

Electronic ISBN: 978-981-10-7323-6

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-981-10-7323-6_16

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner