On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Computation of Capacity Benefit Margin using Differential Evolution
International Journal of Computing Science and Mathematics
Interpolated differential evolution for global optimisation problems
International Journal of Computing Science and Mathematics
Robust multi-user detection based on quantum bee colony optimisation
International Journal of Innovative Computing and Applications
Quantum-Inspired Immune Clonal Algorithm for Global Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved particle swarm optimisation for solving generalised travelling salesman problem
International Journal of Computing Science and Mathematics
Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery
International Journal of Remote Sensing
A simple quantum-inspired bee colony algorithm for discrete optimisation problems
International Journal of Computer Applications in Technology
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The spatial dependence theory is basic theory of sub-pixel mapping SPM. A sub-pixel/pixel spatial attraction model SPSAM can realise the spatial dependence theory directly, however, the results created by SPSAM are noisy and the accuracy is limited. In this paper, a method based on chaotic quantum bee colony algorithm CQBCA is proposed to realise SPM. The proposed method contained two main steps: SPSAM is used to generate the initial result, and CQBCA as the post-process method to improve the SPSAM. Experimental results reveal that the proposed method can provide higher accuracy and reduce the noise in the results created by SPSAM. Furthermore, when compared with the particle swarm optimisation-based sub-pixel mapping, the proposed method often yields better accuracy results.