| Abstract:The traditional artificial fish swarm algorithm in the optimization process, the early convergence speed is very fast, but with the continuous iteration, the convergence speed will gradually decline, it is easy to fall into the local optimal cannot jump out of the situation. Foraging behavior of fish directly affects the convergence speed and the accuracy of numerical solution in the later stage of the algorithm, and field of vision and step size are the basis of foraging behavior of artificial fish. In the early stage, a wide field of vision and a large step size are needed, and in the later stage, the field of vision and step size are limited to improve the convergence speed and optimization accuracy. Vision based adaptive damping function and adaptive step attenuation function to ensure accuracy and global convergence speed of optimal solution, through weighting factor to determine individual biological behavior choice of fish, reuse Levy to swimming mechanism to improve the global search capability of the artificial fish, realized the multidimensional improvement of traditional artificial fish algorithm. Finally, a simulation experiment is designed to verify the efficiency and superiority of the proposed algorithm. |
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