Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a gen...Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a genetic algorithm （GA） and fuzzy discrete particle swarm optimization （FDPSO） are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical （AIC）, particle swarm optimization （PSO）, and genetic algorithm with particle swarm optimization （GA-PSO）.展开更多
In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algori...In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algorithm（GA） is proposed to optimize the sub-carriers and bits allocation. In the algorithm, a random velocity between the maximum and minimum particle velocity is used as the updating velocity instead of maximum or minimum velocity when the updated particle velocity is higher than the maximum particle velocity or lower than the minimum particle velocity. Then, the convergence population is used as the initial population of the genetic algorithm to optimize the subcarriers and bits allocation further. Simulation results show that the transmitted power of the proposed algorithm is about 2 d B to 10 d B lower than that of the genetic algorithm, particle swarm optimization algorithm, and Zhang＇s algorithm.展开更多
文摘Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a genetic algorithm （GA） and fuzzy discrete particle swarm optimization （FDPSO） are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical （AIC）, particle swarm optimization （PSO）, and genetic algorithm with particle swarm optimization （GA-PSO）.
基金supported by the National Natural Science Foundation of China under Grant No.61371112
文摘In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algorithm（GA） is proposed to optimize the sub-carriers and bits allocation. In the algorithm, a random velocity between the maximum and minimum particle velocity is used as the updating velocity instead of maximum or minimum velocity when the updated particle velocity is higher than the maximum particle velocity or lower than the minimum particle velocity. Then, the convergence population is used as the initial population of the genetic algorithm to optimize the subcarriers and bits allocation further. Simulation results show that the transmitted power of the proposed algorithm is about 2 d B to 10 d B lower than that of the genetic algorithm, particle swarm optimization algorithm, and Zhang＇s algorithm.
