@Article{cmc.2020.07478,
AUTHOR = {Tao Wu, Lei Xie, Xi Chen, Amir Homayoon Ashrafzadeh, Shu Zhang},
TITLE = {A Novel Quantum-Behaved Particle Swarm Optimization Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {873--890},
URL = {http://www.techscience.com/cmc/v63n2/38549},
ISSN = {1546-2226},
ABSTRACT = {The efficient management of ambulance routing for emergency requests is vital 
to save lives when a disaster occurs. Quantum-behaved Particle Swarm Optimization 
(QPSO) algorithm is a kind of metaheuristic algorithms applied to deal with the problem of 
scheduling. This paper analyzed the motion pattern of particles in a square potential well, 
given the position equation of the particles by solving the Schrödinger equation and 
proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well (BCQSPSO). In this novel algorithm, the intrinsic cognitive link between particles’ experience 
information and group sharing information was created by using normal Copula function. 
After that, the control parameters chosen strategy gives through experiments. Finally, the 
simulation results of the test functions show that the improved algorithms outperform the 
original QPSO algorithm and due to the error gradient information will not be over utilized 
in square potential well, the particles are easy to jump out of the local optimum, the BCQSPSO is more suitable to solve the functions with correlative variables.},
DOI = {10.32604/cmc.2020.07478}
}