Artificial fish swarm algorithm (AFSA) is a stochastic global optimization technique proposed lately.After analyzing the disadvantages of AFSA

this paper presents a hybrid artificial fish swarm optimization algorithm based on mutation operator and simulated annealing.The method is divided into two phases:the AFSA with mutation operator is used to search for the optimum solution

and simulated annealing is applied to optimize the optimum solution.By adding the mutation operator to AFSA in the evolution process

the ability of AFSA to break away from artificial fish stochastic moving without a definite purpose or heavy getting together round the local optimum solution is greatly improved.The hybrid algorithm is as simple for implement as AFSA

but can greatly improve the ability of seeking the global excellent result and convergence property and accuracy.The feasibility and effectiveness of our approach was verified through testing by function and practical problem.The experimental results show that the proposed algorithm is significantly superior to original AFSA.