Abstract
The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size.
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.
