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Quantum Bacterial Foraging Optimization Algorithm
Nanjing, Jiangsu, Peoples R China.
Nanjing, Jiangsu, Peoples R China.
Nanjing, Jiangsu, Peoples R China.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
2014 (English)In: 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE Press, 2014, 1265-1272 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step to drive the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. The numeric results show that the proposed QBFO has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. In addition, we applied our proposed QBFO to solve the traveling salesman problem, which is a well-known NP-hard problem in combinatorial optimization. The results indicate that the proposed QBFO shows better convergence behavior without premature convergence, and has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution, as compared to ant colony optimization algorithm and quantum genetic algorithm.

Place, publisher, year, edition, pages
IEEE Press, 2014. 1265-1272 p.
Keyword [en]
quantum computing, bacterial foraging optimization, quantum bacterial foraging optimization, aveling salesman problem
National Category
Physical Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:umu:diva-106806ISI: 000356684601088ISBN: 978-1-4799-1488-3 (print)OAI: oai:DiVA.org:umu-106806DiVA: diva2:846857
Conference
IEEE Congress on Evolutionary Computation (CEC), Beijing, PEOPLES R CHINA, JULY 06-11, 2014
Available from: 2015-08-18 Created: 2015-08-07 Last updated: 2015-08-18Bibliographically approved

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Li, Haibo

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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