TY - GEN
T1 - Improved quantum particle swarm optimization by bloch sphere
AU - Du, Yu
AU - Duan, Haibin
AU - Liao, Renjie
AU - Li, Xihua
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Bloch QPSO(BQPSO)
KW - Bloch Sphere
KW - Quantum Particle Swarm Optimization (QPSO)
KW - global search
UR - https://www.scopus.com/pages/publications/77954642228
U2 - 10.1007/978-3-642-13495-1_17
DO - 10.1007/978-3-642-13495-1_17
M3 - 会议稿件
AN - SCOPUS:77954642228
SN - 3642134947
SN - 9783642134944
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 143
BT - Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings
T2 - 1st International Conference on Advances in Swarm Intelligence, ICSI 2010
Y2 - 12 June 2010 through 15 June 2010
ER -