Spectrum Sharing with Vehicular Communication in Cognitive Small-Cell Networks

Algorithm 1

The proposed genetic simulated annealing algorithm.

(1)	Initialization: set the evolution algebra counter to 0. Set the initial value of the population which includes all random sets of strategies (genes), and give the initial annealing temperature.
(2)	Calculate current population fitness and population statistics, and further evaluate fitness by price bounds that defined the demand function.
(3)	Use elite selection mechanism to choose the fittest strategy on a higher value according to profit function.
(4)	Apply crossover and mutation on individuals by adaptive probability, and then preserve optimal operation to generate new individual of price strategy (offspring).
(5)	Take the individuals in step 3 as input to perform simulated annealing operation, and replace dissatisfied individuals.
(6)	Sort the results after performing the simulated annealing algorithm, and take the optimal results as a new population.
(7)	Evaluate individuals by profit function and judge convergence conditions. If the current loop parameter does not satisfy the convergence condition, go to step 2. Conversely, the solution process is completed if the convergence condition is met.