Research On Multimodal Function Based On Evolutionary Algorithm

Multimodal function is containing more than one local optimal solutions and global optimal solutions. There are many research questions need to be transformed into multi-modal function problems in mathematics, architecture, engineering, mechanical and other practical areas. Such as neural network structure and weights optimization, complex system parameters and structure identification problems etc. solving these realities nature of the problem and the solution will be turned into a multimodal function global optimization problems. Which has become a hot spot and is made a lot of different directions results.This paper focus on the parallelism, efficiency and simplicity of the evolutionary computation for solving these questions, the main research tasks as follows:(1)Research a variety of methods for multi-modal function from traditional methods and evolutionary computation and make a status analysis about it.(2) Make research and analysis on the development, type and respective characteristics of evolutionary computation.(3)This paper put over a new multi-level and omni-bearing method (GSGL algorithm) for multimodal function solving based on the evolutionary computation already referred. Firstly this algorithm processing of the initial population based on shared genetic algorithm models, which could ensure the diversity of species thus avoid the premature population.(4) The algorithm use the fuzzy clustering method to populate sub-blocks, each piece is seen as a small population and internal implementation made simultaneously. In the process, the explored optimal solution is added into the file based on the decision condition. This algorithm have the combination ability of Shared genetic algorithm, global search and local search, so it has better results than many others in solving multimodal function.(5) GSGL algorithm is applied to several typical problems of multi-modal function and though analyzing and summarizing experimental results got the algorithm traits. The papers also make the prospect for the future.