The Improvement Of Artificial Fish Swarm Algorithm And Its Application In Traveling Salesman Problem

The optimization problem extends to every corner of life.There are a lot of complex optimization problems in the field of computer science,electronic engineering,artificial intelligence,communication technology,signal processing and other fields,including multi dimension,multi variable,multi condition,multi peak,multi-objective problem.However,there are many methods to solve the optimization problems,such as the classical algorithm,the construction algorithm,the local search algorithm,the directed search algorithm,the method based on the dynamic evolution of the system,the swarm intelligence algorithm and so on.It is not ideal to solve the complex optimization problems of multi variable,multi dimension,multi condition,multi peak and multi targets with the classical algorithm and the construction algorithm.They don't even work.The application of the guidance search algorithm for this kind of complex optimization problems is more effective,more extensive,more stable and more efficient.Artificial fish swarm algorithm(AFSA)is a swarm intelligence optimization algorithm based on the behavior of fish,which is an effective optimization algorithm.This algorithm has the advantages of strong global search ability,fast convergence speed,strong robustness,low initial value and easy to implement.It has been widely used in many fields,such as communication,signal processing,data mining,control and so on.More and more domestic and international problems are solved and the results are very good.However,the artificial fish swarm algorithm is relatively young.Its theoretical foundation is weak,the parameters are not set,the convergence precision is not high,and it is easy to fall into local extremum.In the future,the algorithm needs further development and improvement.This paper aims to improve the disadvantages of the fish swarm algorithm premature convergence and low search accuracy.The improved algorithm will be applied to the traveling salesman problem(TSP).(1)this paper puts forward a kind of artificial fish algorithm based on gravitational search algorithm(GSA-AFSA).The algorithm introduced the inertial mass calculation of ideas,and applied to the calculation of the center of the prey behavior and the swarmbehavior.The performance of the algorithm is improved effectively.Finally,the superiority of the algorithm is verified by the simulation of classical function.(2)This paper proposes a multiple populations of the artificial fish swarm algorithm.In this algorithm,the whole fish swarm is divided into sub fish,and each sub fish is executed independently and parallelly.The performance of the algorithm is improved effectively.Finally,the superiority of the algorithm is verified by the simulation of classical function.(3)The Mutli-AFSA algorithm is applied to solve the traveling salesman problem(TSP).By giving a brief introduction to the idea of solving the TSP problem and giving examples,the feasibility and effectiveness of the Mutli-AFSA algorithm to solve the traveling salesman problem will be verified.Finally,the thesis summarizes the full text comprehensively and systematically.And it puts forward the following research which need to be strengthened and improved.