Research On Multimodal Optimization Based On Crowding DE And Its Application

Multi-modal optimization is a new subject and exists in scientific research and production applications. After building models of such aviation and aerospace, net communication, life scientific problems, it can be abstracted into a function optimization problem. However, the best solution is not always useful in real world, and the optimal solution is also needed.A Crowding Differential Evolution Algorithm is presented and applied to multimodal function optimization for finding all the extreme solutions, which uses DE's global search strategy and internal parallel pattern, through the high CF value search, avoiding the replacement error, maintaining species diversity, can accurately locate all the optimal solutions and extreme solutions. Meanwhile, this algorithm has a lot of advantages such as parameter less, operator simple and swift convergence rate. The algorithm is compared with Crowding Genetic Algorithm and experiment results show that CDE is better to CGA in both convergence rate and convergence accuracy.In the MIMO-OFDM system, the allocation of combined antenna and sub-carrier according to the channel conditions is the key technology to maximize channel capacity. CDE is also introduced to solve the problem of the allocation of combined antenna and sub-carrier in the MIMO-OFDM system. This algorithm has such characteristics as few parameters and fast convergence, so the best allocation of combined antenna and sub-carrier can be found accurately and quickly and the maximum channel capacity can be achieved by using this algorithm.The allocation of spectrum is also studied in this paper. The paper uses the concept of CDE to solve the problem of dynamic spectrum allocation and get a good result.