Research On The Application Of Artificial Fish Swarm Algorithm

Swarm intelligence optimization algorithm is an important research field in optimization computing field.Nowadays,this algorithm has attracted the attention of many researchers at home and abroad.With the continuous research and exploration of swarm intelligence optimization algorithm in recent years,its application depth and breadth are very rapid development.The swarm intelligence optimization algorithm,as some optimization algorithms,which are more representative of the particle swarm algorithm,firefly algorithm,leapfrog algorithm and ant colony algorithm.On 2002,Chinese scholars Dr.Li Xiaolei through the observation of fish activity,selected from several typical behavior of fish to adapt the living environment in the waters,and proposes a new swarm intelligence optimization algorithm,artificial fish swarm algorithm(AFSA).As an important branch of swarm intelligence optimization algorithm,artificial fish swarm algorithm has a very wide range of applications.The artificial fish swarm algorithm is a random search algorithm,the parameter is not high on the initial value of the algorithm set is also very casual,only the objective function can be solved,the algorithm has the advantages of fast convergence speed,strong robustness,simple and easy to operate.But there are some defects,such as slow convergence rate,low searching precision and inaccurate calculation result.To solve this problem,an artificial fish swarm algorithm based on optimal individual reservation(OIRAFSA)is proposed.Specific work is as follows:1.Detailed description of the artificial fish school algorithm,the process,the specific implementation of the algorithm,as well as the basic artificial fish swarm algorithm's advantages and disadvantages are analyzed.2.To improve the basic artificial fish swarm algorithm,an artificial fish swarm algorithm based on optimal individual retention(OIR-AFSA)is proposed.The improved scheme is: firstly,the new algorithm adopts adaptive mobile step;secondly,retain the best individual in the bulletin board;finally,the re definition of artificial fish swarm algorithm cluster behavior,avoid the convergence rate may slow down the disadvantages.3.The artificial fish swarm algorithm based on the optimal individual reservation(OIR-AFSA)is applied to the combinatorial optimization problem.The classic TSP problem,the 0-1 knapsack problem,and the bin packing problem are selected in this paper.The experimental data show that the improved artificial fish swarm algorithm converges faster to the extreme value of function,speed and avoid the local extreme point more quickly,with excellent advantage in solving complex nonlinear problems in daily life.