Research On Swarm Intelligence Algorithm And Its Applications In Communication

Swarm intelligence algorithms are developped from the natural phenomenons and processes, they have many advantages, such as global searching ability, highly efficient parallel computation, good robustness, etc. Artificial fish swarm algorithm (AFSA) is a novel swarm intelligent optimization algorithm, and now it is used in more and more engineering fields. As a swarm intelligence algorithm, AFSA has its weakness, such as high complexity,low optimizing precision and low convergence speed in the later period of the optimization. To solve the problem, an improved AFSA called global AFSA(GAFSA) is propsed. In the GAFSA, global information is added to the artificial fish position updating process. The experimental results indicate that the optimization precision and the convergence speed of the proposed method are significantly improved when compared with those of the original AFSA. In GAFSA, a novel behavior called swallowing behavior is proposed, and the complexity of the GAFSA decreases obviously.In the multiuser OFDM system, the channel fading parameters of different users are generally different. Adaptive resource allocation for each user according to the instantaneous sub-channel information can improve the utilization efficiency of the system. There are two kinds of resource allocation schemes existing in current OFDM system:static and dynamic resource allocation scheme. In this paper dynamic resource allocation scheme is used. Based on the analysis of the multiuser OFDM system model, the objective function of the adaptive resource allocation problem is designed, and the user fairness is added to the objective function. The results obtained by our method show that the sum capacity is maximized and the user fairness is satisfied.Based on the model of the clustering problem, a clustering algorithm called global artificial fish swarm clustering (GAFSC) algorithm is proposed, and the experimental results show that the GAFSC has very good robustness and very good capacity of searching global optimum. To overcome the weakness of the Fuzzy C-means(FCM) method, a hybrid fuzzy clustering algorithm that incorporates the FCM into the GAFSA is proposed, the simulation results show that the the hybrid fuzzy clustering algorithm avoids the FCM's weakness and takes advantage of the GAFSA's robustness.Nowadays, with the increase of the optimization problem's complexity and scale, a single optimization method sometimes can not solve the problem very well, and the hybrid algorithms of the mature optimization algorithms should be an effectual way. In the last part of this thesis, a hybrid algorithm called simulated annealing-artificial fish swarm algorithm (SA-AFSA) that incorporates the SA into the AFSA is proposed. Its performance is verified by a test function. Be provided with the global searching ability of the AFSA and the local searching ability of the SA, the SA-AFSA has very strong global searching ability and very high precision.