TY - JOUR
AU - Chen, Xuebo
AB - SCIENCE CHINA Information Sciences May 2020, Vol. 63 159201:1–159201:3 . . LETTER https://doi.org/10.1007/s11432-018-9618-2 Hybrid quantum particle swarm optimization algorithm and its application 1,2 2* Yukun WANG & Xuebo CHEN School of Chemical Engineering, University of Science and Technology Liaoning, Anshan 114051, China; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China Received 11 May 2018/Revised 2 August 2018/Accepted 5 September 2018/Published online 10 September 2019 Citation Wang Y K, Chen X B. Hybrid quantum particle swarm optimization algorithm and its application. Sci China Inf Sci, 2020, 63(5): 159201, https://doi.org/10.1007/s11432-018-9618-2 Dear editor, population diversity. Meanwhile, there are no ef- Quantum-behaved particle swarm optimization fective measures to enhance the precision of QPSO (QPSO) is an evolutionary algorithm with quan- in local search [5]. We propose the following im- tum behavior. It can be used to solve optimization provements to enhance the population diversity, problems by establishing a potential well at the increase convergence speed, and improve conver- local attraction point to influence the location of gence precision of the proposed HQPSO algorithm. particles [1, 2]. The algorithm offers many advan- (1) A new local attraction point for HQPSO is tages, such as the requirement of 
TI - Hybrid quantum particle swarm optimization algorithm and its application
JO - Science China Information Sciences
DO - 10.1007/s11432-018-9618-2
DA - 2020-05-01
UR - https://www.deepdyve.com/lp/springer-journals/hybrid-quantum-particle-swarm-optimization-algorithm-and-its-p2C7GrDicb
SP - 1
EP - 3
VL - 63
IS - 5
DP - DeepDyve
ER -