The Research Of Artificial Fish Swarm Algorithm And Its Application

Optimization problems need to be solved in many fields and the fine solutions to the problems may be lead to great economic benefit. In order to solve the optimization problems extensively existing in the society, intelligent optimization algorithms have been developed. As new searching algorithms, intelligent optimization algorithms provide new approaches to get the optimization of some complex systems, which have attracted a lot of attentions from researchers around the world and have been applied in many areas.Artificial fish swarm algorithm is an animal's autonomous method that bases on the principle of artificial intelligent, and essentially is a complex intelligent system. It constructs the simple bottom behaviors of artificial fish firstly, and them makes the global optimum emerge finally through AF individuals'local searching behaviors. AFSA has distributed parallel searching ability. AFSA has been proved to have many advantages, such as insensitivity to the initial values and parameters varying, easy implementation, and so on. However, some shortcomings exist in AFSA, such as slower convergence speed, hard to local all optima for multimodal problem. At present, this algorithm research has already improved many other application and has already become extremely active from research question in the interdisciplinary studies.In the paper, the improving and application of the artificial fish swarm algorithm are mainly discussed. The research results have the important reference significance regarding the application of AFSA to solution actual optimization question. The further research also has the higher reference value to the next research on AFSA. The major research contents are as follows:(1) A brief introduction of intelligent optimization problems and swarm intelligence was made. The biological elements, basic principles, mathematical model and algorithm flow of AFSA were discussed in detail. The research progress of AFSA was summarized by stages. All of the above indicated the importance of researching the AFSA. Through experiments, parameters selection was researched in details, which provided the useful reference for the further study of the AFSA.(2) Aiming at the slow convergence speed of AFSA with larger pop-size, the Proportional choice operator was introduced into basic AFSA. Through screening population of larger pop-size before per iteration, and keeping eximious artificial fishes into iterative optimization, the convergence speed was picked up. The improved AFSA was applied into determining the transverse diffusive coefficient of river, which received preferable result.(3) A global binary artificial fish swarm algorithm was advanced, which substituted the central and global extreme position of the swarm for the artificial fish neighboring central and extreme position, and transformed the ways of executing the behaviors of artificial fish to probability value between 0 to 1, made the artificial fish to choose correct behaviors as the probability value. The GBAFSA was applied into solving 0-1 knapsack problem, the result of solving specific example was better.(4) The global artificial fish swarm algorithm, which was given from reference, was used to be a solution for mechanical design optimization, such as the parameter optimization of stripping header and the retarder of helical spur gears, the better parameter values were received.In conclusion, this paper make a summarization of the prospect of the AFSA. The AFSA has different ways to the traditional method, and they have solid foundations of merging with them, then we hope that it can widely used in future.