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Improved Quantum Particle Swarm Optimization by Bloch Sphere

  • Yu Du
  • Haibin Duan
  • Renjie Liao
  • Xihua Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

Quantum Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method which introduces the Quantum theory into the basic Particle Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encoding mechanism is adopted into QPSO, which can vividly describe the dynamic behavior of the quantum. In this way, the diversity of the swarm can be increased, and the local minima can be effectively avoided. The proposed algorithm, named Bloch QPSO (BQPSO), is tested with PID controller parameters optimization problem. Experimental results demonstrate that BQPSO has both stronger global search capability and faster convergence speed, and it is feasible and effective in solving some complex optimization problems.

Keywords

Quantum Particle Swarm Optimization (QPSO) Bloch Sphere Bloch QPSO(BQPSO) global search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yu Du
    • 1
  • Haibin Duan
    • 1
    • 2
  • Renjie Liao
    • 1
  • Xihua Li
    • 1
  1. 1.National Key Laboratory of Science and Technology on Holistic Control, School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Provincial Key Laboratory for Information Processing TechnologySuzhou UniversitySuzhouChina

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