Quantum-inspired glowworm swarm optimisation and its application
Hongyuan Gao, Yanan Du, Ming Diao · International Journal of Computing Science and Mathematics · 2017
In order to solve discrete optimisation problem, a novel intelligence algorithm called as quantum-inspired glowworm swarm optimisation (QGSO) is proposed. By hybridising the glowworm swarm optimisation, quantum coding and quantum evolutionary theory, the quantum state and binary state of the quantum glowworms can be well evolved by simulated quantum rotation gate. The classical benchmark functions are used to test effectiveness of QGSO. The proposed QGSO algorithm is an effective discrete optimisation algorithm which has better convergent accuracy and speed. Then QGSO is used to resolve thinned array optimisation difficulties. Simulation results are provided to show that the proposed thinned array method based on QGSO is superior to the thinned array methods based on previous classical intelligence algorithms. The proposed thinned array method based on QGSO can search the global optimal solution of thinned array.