Abstract:

Aiming at some defects of the traditional artificial fish swarm algorithm (AFSA), such as low optimization precision and long running time, we propose a new adaptive artificial fish swarm algorithm based on Log-Linear model and Gauss-Cauchy mutation. Firstly, we use the Log-Linear model to improve the three typical behaviors of the artificial fish, which are foraging, clustering and rear-end collision. Secondly, we adopt a policy which can adaptively adjust vision and the step length of the artificial fish in the new algorithm. Thirdly, we leverage the Gauss-Cauchy mutation to keep individual diversity and to avoid falling into the local extremum. Simulation results show that compared with other algorithms, the proposed algorithm has a better convergence speed and stability.

Key words: artificial fish swarm algorithm(AFSA);Log-Linear model;Gauss-Cauchy mutation, adaptive, optimization