Abstract:

One of the basic functions of cognitive engine is to adjust adaptive wireless parameters and use multi-objective optimization strategies to achieve reliable communication in a dynamic environment according to complex wireless environments and service needs. Currently many studies focus on the genetic algorithm (GA) and its improvement, however, its slow convergence is not conducive to complex and high real-time systems. We propose an algorithm, called simulated annealing particle swarm optimization (SABPSO), which combines the simulated annealing algorithm and the particle swarm algorithm to search jointly for a better solution in an alternately iterative manner. It can effectively improve the convergence speed and overcome PSO's shortage of falling into local extreme values easily, thus enhancing the ability of global optimization. Finally, multi-carrier system simulation results in different communication scenarios show that the SABPSO outperforms the basic algorithms in convergence rate and average fitness.

Key words: cognitive engine, multi-objective optimization, particle swarm, simulated annealing