Abstract
In order to improve the convergence performance of Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,this paper proposed an improved QPSO algorithm which was called RE-QPSO based on the random evaluation strategy.The new algorithm evaluated the innovation of particles by using a random factor and improved the ability of the particles to get rid of the local optima.Fixed value strategy and linear decreasing strategy were proposed for controlingthe theunique parameter of QPSO algorithm and they were tested on six benchmark functions.According to the test results,some conclusions concerning the selection of the parameter were drawn.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
