To solve a discrete optimization problem, a quantum-inspired shuffled frog leaping (QSFL) algorithm based on shuffled frog leaping algorithm and quantum information theory is proposed. The QSFL algorithm uses quantum movement equations to find the optimal location by the co-evolution of quantum frog colony. Good performance of the QSFL algorithm is shown by some classical benchmark functions. At the same time, we design an assignment method for cognitive radio spectrum allocation without interference based on it. Simulations are conducted to compare this method with genetic algorithm (GA), quantum genetic algorithm (QGA), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA) and color-sensitive graph coloring (CSGC) using different network utility functions. Simulation results indicate that the proposed method can find the near-optimal solution. It outperforms the color-sensitive graph coloring and the previous intelligent spectrum allocation methods.
GAO Hong-yuan1, CAO Jin-long2
. Quantum-Inspired Shuffled Frog Leaping Algorithm for Spectrum Allocation in Cognitive Radio[J]. Journal of Applied Sciences, 2014
, 32(1)
: 19
-26
.
DOI: 10.3969/j.issn.0255-8297.2014.01.004
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